Online Forecasting of Stock Market Movement Direction Using the Improved Incremental Algorithm

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Online Forecasting of Stock Market Movement Direction Using the Improved Incremental Algorithm"

Transcription

1 Online Forecasting of Stock Market Movement Direction Using the Improved Incremental Algorithm Dalton Lunga and Tshilidzi Marwala University of the Witwatersrand School of Electrical and Information Engineering Private Bag 3 Wits 2050, Johannesburg, South Africa {d.lunga, marwala Abstract. In this paper we present a particular implementation of the Learn++ algorithm: we investigate the predictability of financial movement direction with Learn++ by forecasting the daily movement direction of the Dow Jones. The Learn++ algorithm is derived from the Adaboost algorithm, which is denominated by sub-sampling. The goal of concept learning, according to the probably approximately correct weak model, is to generate a description of another function, called the hypothesis, which is close to the concept, by using a set of examples. The hypothesis which is derived from weak learning is boosted to provide a better composite hypothesis in generalizing the establishment of the final classification boundary. The framework is implemented using multi-layer Perceptron (MLP) as a weak Learner. First, a weak learning algorithm, which tries to learn a class concept with a single input Perceptron, is established. The Learn++ algorithm is then applied to improve the weak MLP learning capacity and introduces the concept of online incremental learning. The proposed framework is able to adapt as new data are introduced and is able to classify. 1 Introduction The financial market is a complex, evolutionary, and non-linear dynamical system. The field of financial forecasting is characterized by data intensity, noise, non-stationary, unstructured nature, high degree of uncertainty, and hidden relationships [1]. Many factors interact in finance including political events, general economic conditions, and traders expectations. Therefore, predicting market price movements is quite difficult. Increasingly, according to academic investigations, movements in market prices are not random. Rather, they behave in a highly nonlinear and dynamical manner. The standard random walk assumption of future prices may merely be a veil of randomness that shrouds a noisy nonlinear process [2]. Incremental learning is the solution to such scenarios, which can be defined as the process of extracting new information without losing prior I. King et al. (Eds.): ICONIP 2006, Part III, LNCS 4234, pp , c Springer-Verlag Berlin Heidelberg 2006

2 Online Forecasting of Stock Market Movement Direction 441 knowledge from an additional dataset that later becomes available. Various definitions and interpretations of incremental learning can be found in literature, including online learning [3], relearning of previously misclassified instances, and growing and pruning of classifier architectures [4]. An algorithm possesses incremental learning capabilities, if it meets the following criteria: Ability to acquire additional knowledge when new stock data are introduced Ability to retain previously learned information about the stock closing prices. Ability to learn new classes of stock data if introduced by new data. Some applications of online classification problems have been reported recently [5]. In most cases, the degree of accuracy and the acceptability of certain classifications are measured by the error of misclassified instances. Although Learn++ has mostly been applied to classification problems, we show in this paper that the choice of Learn++ algorithm can boost a weak learning model to classify stock closing values with minimum error and reduced training time. For the practitioners in financial market, forecasting methods based on minimizing forecast error may not be adequate to meet their objectives. In other words, trading driven by a certain forecast with a small forecast error may not be as profitable as trading guided by an accurate prediction of the direction of movement. The main goal of this study is to explore the predictability of financial market movement direction using an ensemble of classifiers implemented using the Learn++ algorithm. This paper discusses the ensemble systems, introduces the basic theory on incremental learning and the Learn++ algorithm, and gives the experimental scheme as well as results obtained. 2 Ensemble of Classifiers Ensemble systems have attracted a great deal of attention over the last decade due to their empirical success over single classifier systems on a variety of applications. Such systems combine an ensemble of generally weak classifiers to take advantage of the so-called instability of the weak classifier. This causes the classifiers to construct sufficiently different decision boundaries for minor modifications in their training parameters and as a result each classifier makes different errors on any given instance. A strategic combination of these classifiers, such as weighted majority voting [6], then eliminates the individual errors, generating a strong classifier. A rich collection of algorithms has been developed using multiple classifiers, such as AdaBoost [7], with the general goal of improving the generalization performance of the classification system. Using multiple classifiers for incremental learning, however, has been largely unexplored. Learn++, in part inspired by AdaBoost, was developed in response to recognizing the potential feasibility of ensemble of classifiers in solving the incremental learning problem. Learn++ was initially introduced in [8] as an incremental learning algorithm for the MLP type networks. A more versatile form of the algorithm was presented in [9] for all supervised classifiers. We have recently recognized that the

3 442 D. Lunga and T. Marwala inherent voting mechanism of the algorithm can also be used in effectively determining the confidence of the classification system in its own decision making. In this work, we describe the algorithm Learn++, along with representative results on incremental learning and confidence estimation obtained on the application of the algorithm to predict the direction of the movement for the Dow Jones Average Indicators. 3 Incremental Learning An incremental learning algorithm is defined as an algorithm that learns new information from unseen data, without necessitating access to previously used data [10]. The algorithm must also be able to learn new information from new data and still retains knowledge from the original data. Lastly, the algorithm must be able to learn new classes that may be introduced by new data. This type of learning algorithm is sometimes referred to as a memoryless online learning algorithm. Learning new information without requiring access to previously used data, however, raises stability-plasticity dilemma [11]. This dilemma indicates that a completely stable classifier maintains the knowledge from previously seen data, but fails to adjust in order to learn new information, while a completely plastic classifier is capable of learning new data but lose prior knowledge. The problem with the MLP is that it is a stable classifier and is not able to learn new information after it has been trained. Different procedures have been implemented for incremental learning. One procedure of learning new information from additional data involves discarding the existing classifier and training a new classifier using accumulated data. Other methods such as pruning of networks or controlled modification of classifier weight or growing of classifier architectures are referred to as incremental learning algorithm. This involves modifying the weights of the classifier using the misclassified instances only. The above algorithms are capable of learning new information; however, they suffer from catastrophic forgetting and require access to old data. One approach evaluates the current performance of the classifier architecture. If the present architecture does not sufficiently represent the decision boundaries being learned, new decision clusters are generated in response to new pattern. Furthermore, this approach does not require access to old data and can accommodate new classes. However, the main shortcomings of this approach are: cluster proliferation and extreme sensitivity to selection of algorithm parameters. In this paper, Learn++ is implemented for online prediction of stock movement direction using the Dow Jones average indicators. The Learn++ algorithm is summarized in the next section. 4 Learn++ Learn++ is an incremental learning algorithm that uses an ensemble of classifiers that are combined using weighted majority voting. Learn++ was developed from an inspiration by a boosting algorithm called adaptive boosting (AdaBoost).

4 Online Forecasting of Stock Market Movement Direction 443 Each classifier is trained using a training subset that is drawn according to a distribution D. The classifiers are trained using a weaklearn algorithm. The requirement for the weaklearn algorithm is that it must be able to give a classification rate of atleast 50% initially. For each database D k that contains learning examples and their corresponding classes, Learn++ starts by initializing the weights, w, according to the distribution D T,whereT is the number of hypothesis. Initially the weights are initialized to be uniform, which gives equal probability for all instances to be selected to the first training subset and the distribution is given by D = 1 (1) m Where m represents the number of training examples in database S k. The training data are then divided into training subset T R and testing subset T E to ensure weaklearn capability. The distribution is then used to select the training subset T R and testing subset T E from S k. After the training and testing subset have been selected, the weaklearn algorithm is implemented. The weaklearner is trained using subset, T R.Ahypothesis,h t obtained from weaklearner is tested using both the training and testing subsets to obtain an error,ɛ t : ɛ t = t:h t(x i) y i D t (i) (2) The error is required to be less than 1 2 ; a normalized error β t is computed using: β t = ɛ t (3) 1 ɛ t If the error is greater than 1 2, the hypothesis is discarded and new training and testing subsets are selected according to D T and another hypothesis is computed. All classifiers generated so far, are combined using weighted majority voting to obtain composite hypothesis, H t H t =argmax y Y t:h t(x)=y log 1 β t (4) Weighted majority voting gives higher voting weights to a hypothesis that performs well on its training and testing subsets. The error of the composite hypothesis is computed as in Eq. 5 and is given by E t = t:h t(x i) y i D t (i) (5) If the error is greater than 1 2, the current composite hypothesis is discarded and the new training and testing data are selected according to the distribution D T. Otherwise, if the error is less than 1 2, the normalized error of the composite hypothesis is computed as: B t = E t 1 E t (6)

5 444 D. Lunga and T. Marwala The error is used in the distribution update rule, where the weights of the correctly classified instances are reduced, consequently increasing the weights of the misclassified instances. This ensures that instances that were misclassified by the current hypothesis have a higher probability of being selected for the subsequent training set. The distribution update rule is given by w t+1 = w t (i) B [ Ht(xi) yi ] t (7) Once the T hypotheses are created for each database, the final hypothesis is computed by combining the composite hypothesis using weighted majority voting given by H t =argmax y Y 5 Confidence Measurement K k=1 t:h t(x)=y log 1 β t (8) An intimately relevant issue is the confidence of the classifier in its decision, with particular interest on whether the confidence of the algorithm improves as new data become available. The voting mechanism inherent in Learn++ hints to a practical approach for estimating confidence: decisions made with a vast majority of votes have better confidence than those made by a slight majority [12]. We have implemented McIver and Friedl s weighted exponential voting based confidence metric [13] with Learn++ as C i (x) =P (y = i x) = exp Fi(x) N k=1 expf k(x), 0 C i(x) 1 (9) Where C i (x) is the confidence assigned to instance x when classified as class i, F i (x) is the total vote associated with the i t h class for the instance x and N is the number of classes. The total vote F i (x) class received for any given instances is computed as F i (x) = N ( log 1 t=1 β t, if h t (x) =i 0, otherwise ) (10) The confidence of winning class is then considered as the confidence of the algorithm in making the decision with respect to the winning class. Since C i (x) is between 0 and 1, the confidences can be translated into linguistic indicators as shown in Table 1. These indicators are adopted and used in interpreting our experimental results. Equations (9) and (10) allow Learn++ to determine its own confidence in any classification it makes. The desired outcome of the confidence analysis is to observe a high confidence on correctly classified instances, and a low confidence on misclassified instances, so that the low confidence can be used to flag those instances that are being misclassified by the algorithm. A second desired outcome is to observe improved confidences on correctly classified instances and reduced confidence on misclassified instances, as new data become available, so that the incremental learning ability of the algorithm can be further confirmed.

6 Online Forecasting of Stock Market Movement Direction 445 Table 1. Confidence estimation representation Confidence range (%) Confidence level 90 C 100 Very High (VH) 80 C<90 High (H) 70 C<80 Medium (M) 60 C<70 Low (L) C<60 Very Low (VL) 6 Forecasting Framework 6.1 Experimental Design In our empirical analysis, we set out to examine the daily changes of the Dow Jones Index. The Dow Jones averages are unique in that they are price weighted rather than market capitalization weighted. Their component weightings are therefore affected only by changes in the stock prices, in contrast with other indexes weightings that are affected by both price changes and changes in the number of shares outstanding [14]. When the averages were initially created, their values were calculated by simply adding up the component stock prices and dividing by the number of components. Later, the practice of adjusting the divisor was initiated to smooth out the effects of stock splits and other corporate actions. The Dow Jones Industrial Average measures the composite price performance of over 30 highly capitalized stocks trading on the New York Stock Exchange (NYSE), representing a broad crosssection of US industries. Trading in the index has gained unprecedented popularity in major financial markets around the world. The increasing diversity of financial instruments related to the Dow Jones Index has broadened the dimension of global investment opportunity for both individual and institutional investors. There are two basic reasons for the success of these index trading vehicles. First, they provide an effective means for investors to hedge against potential market risks. Second, they create new profit making opportunities for market speculators and arbitrageurs. Therefore, it has profound implications and significance for researchers and practitioners alike to accurately forecast the movement direction of stock prices. 6.2 Model Input Selection Most of the previous researchers have employed multivariate input. Several studies have examined the cross-sectional relationship between stock index and macroeconomic variables. The potential macroeconomic input variables which are used by the forecasting models include term structure of interest rates (TS), short-term interest rate (ST), long-term interest rate (LT), consumer price index (CPI), industrial production (IP), government consumption (GC), private consumption (PC), gross national product (GNP) and gross domestic product (GDP). Other macroeconomic variables data are not available for our study. Thus for our study only the closing values of the Index were selected as inputs.

7 446 D. Lunga and T. Marwala A one step forward prediction of the Index was performed on a daily basis. The output of this prediction model was used as inputs to the learn++ algorithm for classification into the correct category that would give an indication of whether the predicted index value is 1 (indicating a positive increase in next day s predicted closing value compared to the previous day s closing value) or a predicted closing value of 1, indicating a decrease in next day s predicted closing value compared to the previous day s closing value. Figure 1 depicts the conceptual model of all processes required for this study. The first prediction model can be written as depicted by Eq. 11 below: CV t = F (cv t 1,cv t 2,cv t 3,cv t 4 ) (11) Where CV t is the predicted close value at time t, cv t 1 indicates the close value at day i, wherei =1, 2, 3,,t 1.The second model takes the output of the first model as its input in predicting the direction of movement for the index. The classification prediction stage can be represented by Eq. 12: Direction t = F (CV t ) (12) Where CV t is the first model prediction of the fifth day stock closing value when given the raw data at time t 1tot 4 respectively. Direction t is a categorical variable to indicate the movement direction of Dow Jones Index at time t. If Dow Jones Index at time t is larger than that at time t 1, Direction t is 1. Otherwise, Direction t is 1. Fig. 1. Proposed model for online stock forecasting 6.3 Experimental Results The forecasting model described in the sections above is estimated and validated by insample data. The model estimation selection process is then followed by an empirical evaluation which is based on the out-of-sample data. At this stage, the relative performance of the model is measured by the classification accuracy of the final hypothesis chosen for all given databases. The confidence of the algorithm on its own decision is used in establishing the accuracy of predicted closing value category. The first experiment implements a one step forward prediction of the next day s stock closing value. After predicting the

8 Online Forecasting of Stock Market Movement Direction 447 next day s closing value this value is fed into a classification model to indicate the direction of movement for the stock prices. As discussed above the database consisted of 1476 instances of the Dow Jones average closing value during the period from January 2000 to November 2005; 1000 instances is used for training and all the remaining instances is used for validation. The two binary classes are 1, indicating an upward direction of returns in Dow Jones stock, and -1 to indicate a predicted fall/downward direction of movement for the Dow Jones stock. Four datasets S 1, S 2, S 3, S 4, where each dataset included exactly one quarter of the entire training data, were provided to Learn++ in four training sessions for incremental learning. For each training session k,(k =1, 2, 3, 4) three weak hypothesis were generated by Learn ++. Each hypothesis h 1, h 2 and h 3 of the k t h training session was generated using a training subset TR t and a testing subset TE t. The WeakLearner was a single hidden layer MLP with 15 hidden layer nodes and 1 output node with an MSE goal of 0.1. The test set of data, Validate consisted of 476 instances that were used for validation purposes. On average, the MLP hypothesis, weaklearner, performed little over 50%, which improved to over 80% when the hypothesis were combined by making use of weighted majority voting. This improvement demonstrates the performance improvement property of Learn++, as inherited from AdaBoost, on a given database. The data distribution and the percentage classification performance are given in Table 2. The performances listed are on the validation data, Validate following each training session. Table 3 provides an actual breakdown of correctly classified and misclassified instances falling into each confidence range after each training session. The trends of the confidence estimates after subsequent training sessions are given in Table 3. The desired outcome on the actual confidences is high to very high confidences on correctly classified instances, and low to very low confidences on misclassified instances. The desired outcome on confidence trends is increasing or steady confidences on correctly classified instances, and decreasing confidences on misclassified instances, as new data is introduced. Table 2. Training and generalisation performance of Learn++ Database Class(1) Class(-1) Test Performance (%) S S S S Validate The performance shown in Table 2 indicates that the algorithm is improving its generalization capacity as new data become available. The improvement is modest, however, as majority of the new information is already learned in the first training session. Table 4 indicates that the vast majority of correctly classified instances tend to have very high confidences, with continually improved confidences at consecutive training sessions. While a considerable portion of

9 448 D. Lunga and T. Marwala misclassified instances also had high confidence for this database, the general desired trends of increased confidence on correctly classified instances and decreasing confidence on misclassified ones were notable and dominant, as shown in Table 3. Table 3. Confidence results VH H M VL L Correctly classified S S S S Incorrectly classified S S S S Table 4. Confidence trends for Dow Jones Increasing Steady Decreasing Correctly classified Misclassified Conclusion In this paper, we study the use of an incremental algorithm to predict financial markets movement direction. As demonstrated in our empirical analysis, Learn++ is observed to give good results on converting the weaklearner (MLP) into a strong learning algorithm that has confidence in all its decisions. The Learn++ algorithm is observed to assess the confidence of its own decisions. In general, majority of correctly classified instances had very high confidence estimates while lower confidence values were associated with misclassified instances. Therefore, classifications with low confidences can be used as a flag to further evaluate those instances. Furthermore, the algorithm also showed increasing confidences in correctly classified instances and decreasing confidences in misclassified instances after subsequent training sessions. This is a very comforting outcome, which further indicates that algorithm can incrementally acquire new and novel information from additional data. Acknowledgement This research was fully funded by the National Research Foundation of the Republic of South Africa.

10 Online Forecasting of Stock Market Movement Direction 449 References 1. Carpenter, G., Grossberg, S., Marhuzon, N., Reynolds, J., Rosen, D.: Artmap: A neural network architecture for incremental learning supervised learning of analog multidi-mensional maps. In: Transactions in Neural Networks. Volume 3., IEEE (1992) McNelis, P.D., ed.: Neural Networks in Finance: Gaining the predictive edge in the market. Elsevier Academic Press, Oxford-UK (2005) 3. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Science (1997) 4. Bishop, C., ed.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford-London (1995) 5. Vilakazi, B., Marwala, T., Mautla, R., Moloto, E.: Online bushing condition monitoring using computational intelligence. WSEAS Transactions on Power Systems 1 (2006) Littlestone, N., Warmuth, M.: Weighted majority voting algorithm. information and computer science 108 (1994) Polikar, R., Byorick, J., Krause, S., Marino, A., Moreton, M.: Learn++: A classifier independent incremental learning algorithm. Proceedings of International Joint Conference on Neural Networks (2002) 8. Polikar, R.: Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas sensing electronic noise systems. PhD thesis, Iowa State University, Ames (2000) 9. Freund, Y., Schapire, R.: A short introduction to boosting. Japanese Society for Artificial Intelligence 14 (1999) Polikar, R., Udpa, L., Udpa, S., Honavar, V.: An incremental learning algorithm with confi-dence estimation for automated identification of nde signals. Transactions on Ul-trasonic Ferroelectrics, and Frequency control 51 (2004) Grossberg, S.: Nonlinear neural networks: principles, mechanisms and architectures. Neural Networks 1 (1988) Byorick, J., Polikar, R.: Confidence estimation using the incremental learning algorithm. Lecture notes in computer science 2714 (2003) McIver, D., Friedl, M.: Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods. Transactions on Geoscience and Remote Sensing 39 (2001) 14. Leung, M., Daouk, H., Chen, A.: Forecasting stock indices: a comparison of classification and level estimation models. (International Journal of Forecasting)

Introduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk

Introduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk Ensembles 2 Learning Ensembles Learn multiple alternative definitions of a concept using different training

More information

Ensemble Data Mining Methods

Ensemble Data Mining Methods Ensemble Data Mining Methods Nikunj C. Oza, Ph.D., NASA Ames Research Center, USA INTRODUCTION Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods

More information

Incremental SampleBoost for Efficient Learning from Multi-Class Data Sets

Incremental SampleBoost for Efficient Learning from Multi-Class Data Sets Incremental SampleBoost for Efficient Learning from Multi-Class Data Sets Mohamed Abouelenien Xiaohui Yuan Abstract Ensemble methods have been used for incremental learning. Yet, there are several issues

More information

BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL

BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL The Fifth International Conference on e-learning (elearning-2014), 22-23 September 2014, Belgrade, Serbia BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL SNJEŽANA MILINKOVIĆ University

More information

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05 Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification

More information

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

More information

Model Combination. 24 Novembre 2009

Model Combination. 24 Novembre 2009 Model Combination 24 Novembre 2009 Datamining 1 2009-2010 Plan 1 Principles of model combination 2 Resampling methods Bagging Random Forests Boosting 3 Hybrid methods Stacking Generic algorithm for mulistrategy

More information

Neural Networks for Sentiment Detection in Financial Text

Neural Networks for Sentiment Detection in Financial Text Neural Networks for Sentiment Detection in Financial Text Caslav Bozic* and Detlef Seese* With a rise of algorithmic trading volume in recent years, the need for automatic analysis of financial news emerged.

More information

New Ensemble Combination Scheme

New Ensemble Combination Scheme New Ensemble Combination Scheme Namhyoung Kim, Youngdoo Son, and Jaewook Lee, Member, IEEE Abstract Recently many statistical learning techniques are successfully developed and used in several areas However,

More information

Active Learning with Boosting for Spam Detection

Active Learning with Boosting for Spam Detection Active Learning with Boosting for Spam Detection Nikhila Arkalgud Last update: March 22, 2008 Active Learning with Boosting for Spam Detection Last update: March 22, 2008 1 / 38 Outline 1 Spam Filters

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL

MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL G. Maria Priscilla 1 and C. P. Sumathi 2 1 S.N.R. Sons College (Autonomous), Coimbatore, India 2 SDNB Vaishnav College

More information

Advanced Ensemble Strategies for Polynomial Models

Advanced Ensemble Strategies for Polynomial Models Advanced Ensemble Strategies for Polynomial Models Pavel Kordík 1, Jan Černý 2 1 Dept. of Computer Science, Faculty of Information Technology, Czech Technical University in Prague, 2 Dept. of Computer

More information

CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.

CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore. CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notes

More information

Chapter 11 Boosting. Xiaogang Su Department of Statistics University of Central Florida - 1 -

Chapter 11 Boosting. Xiaogang Su Department of Statistics University of Central Florida - 1 - Chapter 11 Boosting Xiaogang Su Department of Statistics University of Central Florida - 1 - Perturb and Combine (P&C) Methods have been devised to take advantage of the instability of trees to create

More information

Neural Networks and Support Vector Machines

Neural Networks and Support Vector Machines INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines

More information

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes Knowledge Discovery and Data Mining Lecture 19 - Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-19-B &

More information

Using artificial intelligence for data reduction in mechanical engineering

Using artificial intelligence for data reduction in mechanical engineering Using artificial intelligence for data reduction in mechanical engineering L. Mdlazi 1, C.J. Stander 1, P.S. Heyns 1, T. Marwala 2 1 Dynamic Systems Group Department of Mechanical and Aeronautical Engineering,

More information

Operations Research and Knowledge Modeling in Data Mining

Operations Research and Knowledge Modeling in Data Mining Operations Research and Knowledge Modeling in Data Mining Masato KODA Graduate School of Systems and Information Engineering University of Tsukuba, Tsukuba Science City, Japan 305-8573 koda@sk.tsukuba.ac.jp

More information

A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier

A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier G.T. Prasanna Kumari Associate Professor, Dept of Computer Science and Engineering, Gokula Krishna College of Engg, Sullurpet-524121,

More information

L25: Ensemble learning

L25: Ensemble learning L25: Ensemble learning Introduction Methods for constructing ensembles Combination strategies Stacked generalization Mixtures of experts Bagging Boosting CSCE 666 Pattern Analysis Ricardo Gutierrez-Osuna

More information

Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model

Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model Iman Attarzadeh and Siew Hock Ow Department of Software Engineering Faculty of Computer Science &

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

A Multi-level Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study

A Multi-level Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study 211 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (211) (211) IACSIT Press, Singapore A Multi-level Artificial Neural Network for Residential and Commercial Energy

More information

Comparison of Data Mining Techniques used for Financial Data Analysis

Comparison of Data Mining Techniques used for Financial Data Analysis Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract

More information

Stabilization by Conceptual Duplication in Adaptive Resonance Theory

Stabilization by Conceptual Duplication in Adaptive Resonance Theory Stabilization by Conceptual Duplication in Adaptive Resonance Theory Louis Massey Royal Military College of Canada Department of Mathematics and Computer Science PO Box 17000 Station Forces Kingston, Ontario,

More information

CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.

CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning. Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

More information

Chapter 6. The stacking ensemble approach

Chapter 6. The stacking ensemble approach 82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

More information

NTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling

NTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling 1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information

More information

Prediction of Stock Performance Using Analytical Techniques

Prediction of Stock Performance Using Analytical Techniques 136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University

More information

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines

More information

SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK

SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK N M Allinson and D Merritt 1 Introduction This contribution has two main sections. The first discusses some aspects of multilayer perceptrons,

More information

International Journal of Computer Trends and Technology (IJCTT) volume 4 Issue 8 August 2013

International Journal of Computer Trends and Technology (IJCTT) volume 4 Issue 8 August 2013 A Short-Term Traffic Prediction On A Distributed Network Using Multiple Regression Equation Ms.Sharmi.S 1 Research Scholar, MS University,Thirunelvelli Dr.M.Punithavalli Director, SREC,Coimbatore. Abstract:

More information

Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring

Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring 714 Evaluation of Feature election Methods for Predictive Modeling Using Neural Networks in Credits coring Raghavendra B. K. Dr. M.G.R. Educational and Research Institute, Chennai-95 Email: raghavendra_bk@rediffmail.com

More information

Solving Regression Problems Using Competitive Ensemble Models

Solving Regression Problems Using Competitive Ensemble Models Solving Regression Problems Using Competitive Ensemble Models Yakov Frayman, Bernard F. Rolfe, and Geoffrey I. Webb School of Information Technology Deakin University Geelong, VIC, Australia {yfraym,brolfe,webb}@deakin.edu.au

More information

Knowledge Discovery in Stock Market Data

Knowledge Discovery in Stock Market Data Knowledge Discovery in Stock Market Data Alfred Ultsch and Hermann Locarek-Junge Abstract This work presents the results of a Data Mining and Knowledge Discovery approach on data from the stock markets

More information

EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE

EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE S. Anupama Kumar 1 and Dr. Vijayalakshmi M.N 2 1 Research Scholar, PRIST University, 1 Assistant Professor, Dept of M.C.A. 2 Associate

More information

Knowledge Based Descriptive Neural Networks

Knowledge Based Descriptive Neural Networks Knowledge Based Descriptive Neural Networks J. T. Yao Department of Computer Science, University or Regina Regina, Saskachewan, CANADA S4S 0A2 Email: jtyao@cs.uregina.ca Abstract This paper presents a

More information

Incremental Learning

Incremental Learning Incremental Learning Abdelhamid Bouchachia Department of Informatics University of Klagenfurt Universitaetsstr. 65-67 Klagenfurt, 9020 Austria voice: +43 463 2700 3525 fax: +43 463 2700 3599 email: hamid@isys.uni-klu.ac.at

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right

More information

A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication

A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication 2012 45th Hawaii International Conference on System Sciences A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication Namhyoung Kim, Jaewook Lee Department of Industrial and Management

More information

Ensembles and PMML in KNIME

Ensembles and PMML in KNIME Ensembles and PMML in KNIME Alexander Fillbrunn 1, Iris Adä 1, Thomas R. Gabriel 2 and Michael R. Berthold 1,2 1 Department of Computer and Information Science Universität Konstanz Konstanz, Germany First.Last@Uni-Konstanz.De

More information

Boosting. riedmiller@informatik.uni-freiburg.de

Boosting. riedmiller@informatik.uni-freiburg.de . Machine Learning Boosting Prof. Dr. Martin Riedmiller AG Maschinelles Lernen und Natürlichsprachliche Systeme Institut für Informatik Technische Fakultät Albert-Ludwigs-Universität Freiburg riedmiller@informatik.uni-freiburg.de

More information

A Learning Algorithm For Neural Network Ensembles

A Learning Algorithm For Neural Network Ensembles A Learning Algorithm For Neural Network Ensembles H. D. Navone, P. M. Granitto, P. F. Verdes and H. A. Ceccatto Instituto de Física Rosario (CONICET-UNR) Blvd. 27 de Febrero 210 Bis, 2000 Rosario. República

More information

A Data Mining Study of Weld Quality Models Constructed with MLP Neural Networks from Stratified Sampled Data

A Data Mining Study of Weld Quality Models Constructed with MLP Neural Networks from Stratified Sampled Data A Data Mining Study of Weld Quality Models Constructed with MLP Neural Networks from Stratified Sampled Data T. W. Liao, G. Wang, and E. Triantaphyllou Department of Industrial and Manufacturing Systems

More information

How Boosting the Margin Can Also Boost Classifier Complexity

How Boosting the Margin Can Also Boost Classifier Complexity Lev Reyzin lev.reyzin@yale.edu Yale University, Department of Computer Science, 51 Prospect Street, New Haven, CT 652, USA Robert E. Schapire schapire@cs.princeton.edu Princeton University, Department

More information

131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10

131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10 1/10 131-1 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocom

More information

Analecta Vol. 8, No. 2 ISSN 2064-7964

Analecta Vol. 8, No. 2 ISSN 2064-7964 EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,

More information

REVIEW OF ENSEMBLE CLASSIFICATION

REVIEW OF ENSEMBLE CLASSIFICATION Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IJCSMC, Vol. 2, Issue.

More information

Bootstrapping Big Data

Bootstrapping Big Data Bootstrapping Big Data Ariel Kleiner Ameet Talwalkar Purnamrita Sarkar Michael I. Jordan Computer Science Division University of California, Berkeley {akleiner, ameet, psarkar, jordan}@eecs.berkeley.edu

More information

Increasing Classification Accuracy. Data Mining: Bagging and Boosting. Bagging 1. Bagging 2. Bagging. Boosting Meta-learning (stacking)

Increasing Classification Accuracy. Data Mining: Bagging and Boosting. Bagging 1. Bagging 2. Bagging. Boosting Meta-learning (stacking) Data Mining: Bagging and Boosting Increasing Classification Accuracy Andrew Kusiak 2139 Seamans Center Iowa City, Iowa 52242-1527 andrew-kusiak@uiowa.edu http://www.icaen.uiowa.edu/~ankusiak Tel: 319-335

More information

Neural Network based Vehicle Classification for Intelligent Traffic Control

Neural Network based Vehicle Classification for Intelligent Traffic Control Neural Network based Vehicle Classification for Intelligent Traffic Control Saeid Fazli 1, Shahram Mohammadi 2, Morteza Rahmani 3 1,2,3 Electrical Engineering Department, Zanjan University, Zanjan, IRAN

More information

Lecture 6. Artificial Neural Networks

Lecture 6. Artificial Neural Networks Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm

More information

Introduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk

Introduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trakovski trakovski@nyus.edu.mk Neural Networks 2 Neural Networks Analogy to biological neural systems, the most robust learning systems

More information

Hong Kong Stock Index Forecasting

Hong Kong Stock Index Forecasting Hong Kong Stock Index Forecasting Tong Fu Shuo Chen Chuanqi Wei tfu1@stanford.edu cslcb@stanford.edu chuanqi@stanford.edu Abstract Prediction of the movement of stock market is a long-time attractive topic

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Effect of Using Neural Networks in GA-Based School Timetabling

Effect of Using Neural Networks in GA-Based School Timetabling Effect of Using Neural Networks in GA-Based School Timetabling JANIS ZUTERS Department of Computer Science University of Latvia Raina bulv. 19, Riga, LV-1050 LATVIA janis.zuters@lu.lv Abstract: - The school

More information

Ensemble Learning Better Predictions Through Diversity. Todd Holloway ETech 2008

Ensemble Learning Better Predictions Through Diversity. Todd Holloway ETech 2008 Ensemble Learning Better Predictions Through Diversity Todd Holloway ETech 2008 Outline Building a classifier (a tutorial example) Neighbor method Major ideas and challenges in classification Ensembles

More information

ENHANCED CONFIDENCE INTERPRETATIONS OF GP BASED ENSEMBLE MODELING RESULTS

ENHANCED CONFIDENCE INTERPRETATIONS OF GP BASED ENSEMBLE MODELING RESULTS ENHANCED CONFIDENCE INTERPRETATIONS OF GP BASED ENSEMBLE MODELING RESULTS Michael Affenzeller (a), Stephan M. Winkler (b), Stefan Forstenlechner (c), Gabriel Kronberger (d), Michael Kommenda (e), Stefan

More information

HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION

HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION Chihli Hung 1, Jing Hong Chen 2, Stefan Wermter 3, 1,2 Department of Management Information Systems, Chung Yuan Christian University, Taiwan

More information

Using News Articles to Predict Stock Price Movements

Using News Articles to Predict Stock Price Movements Using News Articles to Predict Stock Price Movements Győző Gidófalvi Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 9237 gyozo@cs.ucsd.edu 21, June 15,

More information

Creating Short-term Stockmarket Trading Strategies using Artificial Neural Networks: A Case Study

Creating Short-term Stockmarket Trading Strategies using Artificial Neural Networks: A Case Study Creating Short-term Stockmarket Trading Strategies using Artificial Neural Networks: A Case Study Bruce Vanstone, Tobias Hahn Abstract Developing short-term stockmarket trading systems is a complex process,

More information

Real Stock Trading Using Soft Computing Models

Real Stock Trading Using Soft Computing Models Real Stock Trading Using Soft Computing Models Brent Doeksen 1, Ajith Abraham 2, Johnson Thomas 1 and Marcin Paprzycki 1 1 Computer Science Department, Oklahoma State University, OK 74106, USA, 2 School

More information

Training Methods for Adaptive Boosting of Neural Networks for Character Recognition

Training Methods for Adaptive Boosting of Neural Networks for Character Recognition Submission to NIPS*97, Category: Algorithms & Architectures, Preferred: Oral Training Methods for Adaptive Boosting of Neural Networks for Character Recognition Holger Schwenk Dept. IRO Université de Montréal

More information

Flexible Neural Trees Ensemble for Stock Index Modeling

Flexible Neural Trees Ensemble for Stock Index Modeling Flexible Neural Trees Ensemble for Stock Index Modeling Yuehui Chen 1, Ju Yang 1, Bo Yang 1 and Ajith Abraham 2 1 School of Information Science and Engineering Jinan University, Jinan 250022, P.R.China

More information

International Journal of Electronics and Computer Science Engineering 1449

International Journal of Electronics and Computer Science Engineering 1449 International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN- 2277-1956 Neural Networks in Data Mining Priyanka Gaur Department of Information and

More information

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions

More information

6.2.8 Neural networks for data mining

6.2.8 Neural networks for data mining 6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural

More information

Towards better accuracy for Spam predictions

Towards better accuracy for Spam predictions Towards better accuracy for Spam predictions Chengyan Zhao Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S 2E4 czhao@cs.toronto.edu Abstract Spam identification is crucial

More information

Artificial Neural Network and Non-Linear Regression: A Comparative Study

Artificial Neural Network and Non-Linear Regression: A Comparative Study International Journal of Scientific and Research Publications, Volume 2, Issue 12, December 2012 1 Artificial Neural Network and Non-Linear Regression: A Comparative Study Shraddha Srivastava 1, *, K.C.

More information

On the effect of data set size on bias and variance in classification learning

On the effect of data set size on bias and variance in classification learning On the effect of data set size on bias and variance in classification learning Abstract Damien Brain Geoffrey I Webb School of Computing and Mathematics Deakin University Geelong Vic 3217 With the advent

More information

AUTOMATION OF ENERGY DEMAND FORECASTING. Sanzad Siddique, B.S.

AUTOMATION OF ENERGY DEMAND FORECASTING. Sanzad Siddique, B.S. AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty of the Graduate School, Marquette University, in Partial Fulfillment of the Requirements for the Degree

More information

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 lakshmi.mahanra@gmail.com

More information

ER Volatility Forecasting using GARCH models in R

ER Volatility Forecasting using GARCH models in R Exchange Rate Volatility Forecasting Using GARCH models in R Roger Roth Martin Kammlander Markus Mayer June 9, 2009 Agenda Preliminaries 1 Preliminaries Importance of ER Forecasting Predicability of ERs

More information

Knowledge Discovery from patents using KMX Text Analytics

Knowledge Discovery from patents using KMX Text Analytics Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers

More information

Supply Chain Forecasting Model Using Computational Intelligence Techniques

Supply Chain Forecasting Model Using Computational Intelligence Techniques CMU.J.Nat.Sci Special Issue on Manufacturing Technology (2011) Vol.10(1) 19 Supply Chain Forecasting Model Using Computational Intelligence Techniques Wimalin S. Laosiritaworn Department of Industrial

More information

Random forest algorithm in big data environment

Random forest algorithm in big data environment Random forest algorithm in big data environment Yingchun Liu * School of Economics and Management, Beihang University, Beijing 100191, China Received 1 September 2014, www.cmnt.lv Abstract Random forest

More information

Data quality in Accounting Information Systems

Data quality in Accounting Information Systems Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania

More information

Improved Neural Network Performance Using Principal Component Analysis on Matlab

Improved Neural Network Performance Using Principal Component Analysis on Matlab Improved Neural Network Performance Using Principal Component Analysis on Matlab Improved Neural Network Performance Using Principal Component Analysis on Matlab Junita Mohamad-Saleh Senior Lecturer School

More information

Data Mining Methods: Applications for Institutional Research

Data Mining Methods: Applications for Institutional Research Data Mining Methods: Applications for Institutional Research Nora Galambos, PhD Office of Institutional Research, Planning & Effectiveness Stony Brook University NEAIR Annual Conference Philadelphia 2014

More information

II. RELATED WORK. Sentiment Mining

II. RELATED WORK. Sentiment Mining Sentiment Mining Using Ensemble Classification Models Matthew Whitehead and Larry Yaeger Indiana University School of Informatics 901 E. 10th St. Bloomington, IN 47408 {mewhiteh, larryy}@indiana.edu Abstract

More information

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support

More information

Optimization of technical trading strategies and the profitability in security markets

Optimization of technical trading strategies and the profitability in security markets Economics Letters 59 (1998) 249 254 Optimization of technical trading strategies and the profitability in security markets Ramazan Gençay 1, * University of Windsor, Department of Economics, 401 Sunset,

More information

Neural Networks and Back Propagation Algorithm

Neural Networks and Back Propagation Algorithm Neural Networks and Back Propagation Algorithm Mirza Cilimkovic Institute of Technology Blanchardstown Blanchardstown Road North Dublin 15 Ireland mirzac@gmail.com Abstract Neural Networks (NN) are important

More information

Towards applying Data Mining Techniques for Talent Mangement

Towards applying Data Mining Techniques for Talent Mangement 2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Towards applying Data Mining Techniques for Talent Mangement Hamidah Jantan 1,

More information

Face Recognition For Remote Database Backup System

Face Recognition For Remote Database Backup System Face Recognition For Remote Database Backup System Aniza Mohamed Din, Faudziah Ahmad, Mohamad Farhan Mohamad Mohsin, Ku Ruhana Ku-Mahamud, Mustafa Mufawak Theab 2 Graduate Department of Computer Science,UUM

More information

A New Quantitative Behavioral Model for Financial Prediction

A New Quantitative Behavioral Model for Financial Prediction 2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore A New Quantitative Behavioral Model for Financial Prediction Thimmaraya Ramesh

More information

SEARCH AND CLASSIFICATION OF "INTERESTING" BUSINESS APPLICATIONS IN THE WORLD WIDE WEB USING A NEURAL NETWORK APPROACH

SEARCH AND CLASSIFICATION OF INTERESTING BUSINESS APPLICATIONS IN THE WORLD WIDE WEB USING A NEURAL NETWORK APPROACH SEARCH AND CLASSIFICATION OF "INTERESTING" BUSINESS APPLICATIONS IN THE WORLD WIDE WEB USING A NEURAL NETWORK APPROACH Abstract Karl Kurbel, Kirti Singh, Frank Teuteberg Europe University Viadrina Frankfurt

More information

Recurrent Neural Networks

Recurrent Neural Networks Recurrent Neural Networks Neural Computation : Lecture 12 John A. Bullinaria, 2015 1. Recurrent Neural Network Architectures 2. State Space Models and Dynamical Systems 3. Backpropagation Through Time

More information

The Artificial Prediction Market

The Artificial Prediction Market The Artificial Prediction Market Adrian Barbu Department of Statistics Florida State University Joint work with Nathan Lay, Siemens Corporate Research 1 Overview Main Contributions A mathematical theory

More information

A neural network model to forecast Japanese demand for travel to Hong Kong

A neural network model to forecast Japanese demand for travel to Hong Kong Tourism Management 20 (1999) 89 97 A neural network model to forecast Japanese demand for travel to Hong Kong Rob Law*, Norman Au Department of Hotel and Tourism Management, The Hong Kong Polytechnic University,

More information

Rule based Classification of BSE Stock Data with Data Mining

Rule based Classification of BSE Stock Data with Data Mining International Journal of Information Sciences and Application. ISSN 0974-2255 Volume 4, Number 1 (2012), pp. 1-9 International Research Publication House http://www.irphouse.com Rule based Classification

More information

Designing a Decision Support System Model for Stock Investment Strategy

Designing a Decision Support System Model for Stock Investment Strategy Designing a Decision Support System Model for Stock Investment Strategy Chai Chee Yong and Shakirah Mohd Taib Abstract Investors face the highest risks compared to other form of financial investments when

More information

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree Predicting the Risk of Heart Attacks using Neural Network and Decision Tree S.Florence 1, N.G.Bhuvaneswari Amma 2, G.Annapoorani 3, K.Malathi 4 PG Scholar, Indian Institute of Information Technology, Srirangam,

More information

Data, Measurements, Features

Data, Measurements, Features Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are

More information

Data Mining for Knowledge Management in Technology Enhanced Learning

Data Mining for Knowledge Management in Technology Enhanced Learning Proceedings of the 6th WSEAS International Conference on Applications of Electrical Engineering, Istanbul, Turkey, May 27-29, 2007 115 Data Mining for Knowledge Management in Technology Enhanced Learning

More information

Categorical Data Visualization and Clustering Using Subjective Factors

Categorical Data Visualization and Clustering Using Subjective Factors Categorical Data Visualization and Clustering Using Subjective Factors Chia-Hui Chang and Zhi-Kai Ding Department of Computer Science and Information Engineering, National Central University, Chung-Li,

More information

Manjeet Kaur Bhullar, Kiranbir Kaur Department of CSE, GNDU, Amritsar, Punjab, India

Manjeet Kaur Bhullar, Kiranbir Kaur Department of CSE, GNDU, Amritsar, Punjab, India Volume 5, Issue 6, June 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Multiple Pheromone

More information

Neural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com

Neural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com Neural Network and Genetic Algorithm Based Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com Consider the challenge of constructing a financial market trading system using commonly available technical

More information

' ( ) * +, -.. - '/0112! " " " #$#%#%#&

' ( ) * +, -.. - '/0112!    #$#%#%#& ' ( ) * +, -.. - '/0112! " " " #$#%#%#& !"!#$%&'&% &() * () *%+, %-.!, + %*!", + & /001 (&2 " #!. &3+ 4 *+ 3 (* 5 & 2 %617. 8 911:0;/05 &! 2 (?@911:0;/0

More information