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

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

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

Transcription

1 Data Mining: Bagging and Boosting Increasing Classification Accuracy Andrew Kusiak 2139 Seamans Center Iowa City, Iowa Tel: Bagging g Boosting Meta-learning (stacking) Bagging 1 Bagging 2 Corporate decision-making analogy Sample 1 Classifier 1 New Managers seeks advice of experts in areas that s/he does not have expertise The skills of the advisers should complement each other rather than being duplicative Applies also to boosting Training Bootstrap scheme Sample 2 Sample 3 Classifier 2 Classifier 3 Combined classifier Voting scheme decision (1-1/n) n ~ e -1 =.368, where e =

2 Bagging Procedure Bagging 3 Classifier generation Step 1. Create t sets from a base applying the sampling with replacement scheme. Step 2. Apply a learning to each sample training set. Classification Step 3. For an object with unknown decision, make predictions with each of the t classifiers. Step 4. Select the most frequently predicted decision. Bagging 4 Classification Voting scheme Prediction Averaging scheme Also used Bagging with costs and randomization schemes within learning s (e.g., features with equal value gain) Bagging 5 The effect of combining different classifiers (hypotheses) can be explained with the theory of bias-variance decomposition Bias an error due to a learning Variance an error due to the learned model ( set related) The total expected error of a classifier = Bias + Variance Boosting 1 Bagging Individual models are built separately Boosting Combines models of the same type (e.g., decision tree) and it is iterative, i.e., a new model is influenced by the performance of the previously built model Boosting Uses voting or averaging (similar to bagging) Different boosting s exist 2

3 Boosting 2 Method AdaBoost.M1 which is widely used Assumption: can handle weighted instances (usually handled by randomization schemes for selection of training subsets) By weighting instances, the learning can concentrate on instances with high weights (called hard instances), i.e., incorrectly classified instances Boosting 3 AdaBoost.M1 Algorithm (Outline) All instances are equally weighted A learning is applied The weight of incorrectly classified examples is increased ( hard instances), correctly decreased ( easy instances) The concentrates on incorrectly classified hard instances Some had instances become harder some softer A series of diverse experts (classifiers) is generated based on the reweighed Boosting 4 AdaBoost.M1 Algorithm (Steps) Classifier generation Step 0. Set the weight value, w = 1, and assign it to each object in the training set. For each of t iterations, perform: Step 1. Apply a learning to the weighted training set. Step 2. Compute classification error e for the weighted training set. If e = 0 or e >=.5, then terminate the classifier generation process and go to Step 4; otherwise multiple the weight w of each object by e/(1 e) and normalize the weights of all objects. Classification Step 4. Assign weight q = 0 to each decision (class) to be predicted. Step 5. For each of t (or less) classifiers, add log e/(1 e) to the weight of the decision predicted by the classifier and output the decision with the highest weight. Boosting 4 For e = 0 all training examples (objects) are correctly classified (a perfect classifier) and therefore there is no reason to modify the object weights, i.e., for e/(1 e) = 0 all new weights w become 0. For e =.5, the expression log e/(1 e) = 0, and therefore the weights q = 0 are not be modified and therefore no decision is generated due to high classification error e. 3

4 Training Meta-learning 1 Classifier 1 Creating Meta-training Data Voting Each classifier gets one vote and the majority wins. Test Weighted voting Provides preferential treatment to some voting classifiers. Training 2 Classifier 2 decisions decisions Arbitration An arbitrator makes a selection, if the classifiers can not reach a consensus. decisions Metaclassifier Metalearning Metatraining Combining Decisions produced by different classifiers are combined as one decision. Example (1) 1 Vector 1 High 2 Vector 2 Low 3 Vector 3 High Example (2) Predictions of classifiers 1 and 2 for the training set Object No. Classifier 1 Prediction 1 Vector 1 High 2 Vector 2 Low 3 Vector 3 High Classifier 2 Prediction 4

5 Example (3) Object No. Classifier 1 Prediction Classifier 2 Prediction Training set generated by the class-combiner scheme 1 High, High High 2 High, Low Low 3 Low, Low High Example (4) Object No. Classifier 1 Prediction Classifier 2 Prediction Training set generated by the class-attribute-combiner scheme 1 High, High, Vector 1 High 2 High, Low, Vector 2 Low 3 Low, Low, Vector 3 High Example (5) Example (6) Object No. Classifier 1 Prediction Classifier 2 Prediction Training i set generated by the binary class-attribute-combiner bi scheme Object No. Feature Vector Decision 1 Yes, No, Yes, No High 2 Yes, No, No, Yes Low Binary form of the predictions produced by classifier 1 Object No. Classifier 1 Prediction Feature = High Feature = Low Decision 1 High Yes No High 2 High Yes No Low 3 Low No Yes High 3 No,Yes, No, Yes High 5

6 Meta-learners Distributed Integration of knowledge learned from different and distributed bases. Elimination of inductive bias. Extraction of high level models. Scalability to hierarchical meta-learning. Distributed by partitioning Distributed by nature Data Populations from homogeneously distributed sets Θ i = Θ j = Θ L-learner 1 Θ 1 Homogeneous (Θ i = Θ j, i j - all learners share the same distribution) Heterogeneous (Θ i Θ j,i j) P(D Θ) L-learner 2 Θ 2 M-learner Θ L-learner n Θ n 6

7 from heterogeneously distributed sets Gini Index 1 P(D 1 Θ 1) L-learner 1 Θ t 1 μ t Θ i Θ j P(D Θ ) 2 2 P(D Θ ) n n L-learner 2 Θ t 2 M-learner (Θ, μ) t L-learner n Θ n S = set with n objects c = number of classes in S p j = relative frequency of class j in S t = step number μ models interrelationships between distributions of the local c gini (S) = 1 Σ p j 2 j = 1 Gini Index 2 S 1 = partition 1 of S n 1 = number of objects in S 1 S 2 = partition 2 of S n 2 = number of objects in S 2, where n 2 =(n -n 1 ) a = splitting criterion gini (S, a) = n 1 /n gini (S 1 ) + n 2 /n gini (S 2 ) 7

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

Decision Trees from large Databases: SLIQ

Decision Trees from large Databases: SLIQ Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values

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

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

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

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

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

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

Data Mining Classification: Alternative Techniques. Instance-Based Classifiers. Lecture Notes for Chapter 5. Introduction to Data Mining

Data Mining Classification: Alternative Techniques. Instance-Based Classifiers. Lecture Notes for Chapter 5. Introduction to Data Mining Data Mining Classification: Alternative Techniques Instance-Based Classifiers Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar Set of Stored Cases Atr1... AtrN Class A B

More information

Classification and Regression Trees

Classification and Regression Trees Classification and Regression Trees Bob Stine Dept of Statistics, School University of Pennsylvania Trees Familiar metaphor Biology Decision tree Medical diagnosis Org chart Properties Recursive, partitioning

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

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. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank

Data Mining Practical Machine Learning Tools and Techniques. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Engineering the input and output Attribute selection Scheme independent, scheme

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Evaluating the Accuracy of a Classifier Holdout, random subsampling, crossvalidation, and the bootstrap are common techniques for

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

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data

More information

Decompose Error Rate into components, some of which can be measured on unlabeled data

Decompose Error Rate into components, some of which can be measured on unlabeled data Bias-Variance Theory Decompose Error Rate into components, some of which can be measured on unlabeled data Bias-Variance Decomposition for Regression Bias-Variance Decomposition for Classification Bias-Variance

More information

Classification and Prediction

Classification and Prediction Classification and Prediction Slides for Data Mining: Concepts and Techniques Chapter 7 Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 10 Sajjad Haider Fall 2012 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right

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

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

Classification: Basic Concepts, Decision Trees, and Model Evaluation. General Approach for Building Classification Model

Classification: Basic Concepts, Decision Trees, and Model Evaluation. General Approach for Building Classification Model 10 10 Classification: Basic Concepts, Decision Trees, and Model Evaluation Dr. Hui Xiong Rutgers University Introduction to Data Mining 1//009 1 General Approach for Building Classification Model Tid Attrib1

More information

Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2. Tid Refund Marital Status

Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2. Tid Refund Marital Status Data Mining Classification: Basic Concepts, Decision Trees, and Evaluation Lecture tes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Classification: Definition Given a collection of

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

Data Mining for Knowledge Management. Classification

Data Mining for Knowledge Management. Classification 1 Data Mining for Knowledge Management Classification Themis Palpanas University of Trento http://disi.unitn.eu/~themis Data Mining for Knowledge Management 1 Thanks for slides to: Jiawei Han Eamonn Keogh

More information

CS570 Data Mining Classification: Ensemble Methods

CS570 Data Mining Classification: Ensemble Methods CS570 Data Mining Classification: Ensemble Methods Cengiz Günay Dept. Math & CS, Emory University Fall 2013 Some slides courtesy of Han-Kamber-Pei, Tan et al., and Li Xiong Günay (Emory) Classification:

More information

COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction

COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction COMP3420: Advanced Databases and Data Mining Classification and prediction: Introduction and Decision Tree Induction Lecture outline Classification versus prediction Classification A two step process Supervised

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

Data Mining with R. Decision Trees and Random Forests. Hugh Murrell

Data Mining with R. Decision Trees and Random Forests. Hugh Murrell Data Mining with R Decision Trees and Random Forests Hugh Murrell reference books These slides are based on a book by Graham Williams: Data Mining with Rattle and R, The Art of Excavating Data for Knowledge

More information

LCs for Binary Classification

LCs for Binary Classification Linear Classifiers A linear classifier is a classifier such that classification is performed by a dot product beteen the to vectors representing the document and the category, respectively. Therefore it

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

Supervised Learning (Big Data Analytics)

Supervised Learning (Big Data Analytics) Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used

More information

CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka

CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training

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

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

Data Mining Techniques for Prognosis in Pancreatic Cancer

Data Mining Techniques for Prognosis in Pancreatic Cancer Data Mining Techniques for Prognosis in Pancreatic Cancer by Stuart Floyd A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUE In partial fulfillment of the requirements for the Degree

More information

Estimating Missing Attribute Values Using Dynamically-Ordered Attribute Trees

Estimating Missing Attribute Values Using Dynamically-Ordered Attribute Trees Estimating Missing Attribute Values Using Dynamically-Ordered Attribute Trees Jing Wang Computer Science Department, The University of Iowa jing-wang-1@uiowa.edu W. Nick Street Management Sciences Department,

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

Gerry Hobbs, Department of Statistics, West Virginia University

Gerry Hobbs, Department of Statistics, West Virginia University Decision Trees as a Predictive Modeling Method Gerry Hobbs, Department of Statistics, West Virginia University Abstract Predictive modeling has become an important area of interest in tasks such as credit

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

Tree Ensembles for Predicting Structured Outputs

Tree Ensembles for Predicting Structured Outputs Pattern Recognition Pattern Recognition 00 (2012) 1 26 Tree Ensembles for Predicting Structured Outputs Dragi Kocev a,, Celine Vens b, Jan Struyf b, Sašo Džeroski a,c,d a Department of Knowledge Technologies,

More information

Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries

Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries 2015 SURE Presentation September 22 nd, 2015 Lau Ka Tung Department of Physics, The Chinese University of Hong Kong

More information

Data Mining Classification: Decision Trees

Data Mining Classification: Decision Trees Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous

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

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

Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods

Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods Jerzy B laszczyński 1, Krzysztof Dembczyński 1, Wojciech Kot lowski 1, and Mariusz Paw lowski 2 1 Institute of Computing

More information

Metalearning for Dynamic Integration in Ensemble Methods

Metalearning for Dynamic Integration in Ensemble Methods Metalearning for Dynamic Integration in Ensemble Methods Fábio Pinto 12 July 2013 Faculdade de Engenharia da Universidade do Porto Ph.D. in Informatics Engineering Supervisor: Doutor Carlos Soares Co-supervisor:

More information

New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction

New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction Introduction New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.

More information

6. If there is no improvement of the categories after several steps, then choose new seeds using another criterion (e.g. the objects near the edge of

6. If there is no improvement of the categories after several steps, then choose new seeds using another criterion (e.g. the objects near the edge of Clustering Clustering is an unsupervised learning method: there is no target value (class label) to be predicted, the goal is finding common patterns or grouping similar examples. Differences between models/algorithms

More information

D-optimal plans in observational studies

D-optimal plans in observational studies D-optimal plans in observational studies Constanze Pumplün Stefan Rüping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational

More information

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA Welcome Xindong Wu Data Mining: Updates in Technologies Dept of Math and Computer Science Colorado School of Mines Golden, Colorado 80401, USA Email: xwu@ mines.edu Home Page: http://kais.mines.edu/~xwu/

More information

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d. EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

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

Decision tree algorithm short Weka tutorial

Decision tree algorithm short Weka tutorial Decision tree algorithm short Weka tutorial Croce Danilo, Roberto Basili Machine leanring for Web Mining a.a. 2009-2010 Machine Learning: brief summary Example You need to write a program that: given a

More information

Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr

Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr WEKA Gallirallus Zeland) australis : Endemic bird (New Characteristics Waikato university Weka is a collection

More information

Supervised Learning with Unsupervised Output Separation

Supervised Learning with Unsupervised Output Separation Supervised Learning with Unsupervised Output Separation Nathalie Japkowicz School of Information Technology and Engineering University of Ottawa 150 Louis Pasteur, P.O. Box 450 Stn. A Ottawa, Ontario,

More information

Distributed Regression For Heterogeneous Data Sets 1

Distributed Regression For Heterogeneous Data Sets 1 Distributed Regression For Heterogeneous Data Sets 1 Yan Xing, Michael G. Madden, Jim Duggan, Gerard Lyons Department of Information Technology National University of Ireland, Galway Ireland {yan.xing,

More information

Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors

Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors Classification k-nearest neighbors Data Mining Dr. Engin YILDIZTEPE Reference Books Han, J., Kamber, M., Pei, J., (2011). Data Mining: Concepts and Techniques. Third edition. San Francisco: Morgan Kaufmann

More information

A Study of Detecting Credit Card Delinquencies with Data Mining using Decision Tree Model

A Study of Detecting Credit Card Delinquencies with Data Mining using Decision Tree Model A Study of Detecting Credit Card Delinquencies with Data Mining using Decision Tree Model ABSTRACT Mrs. Arpana Bharani* Mrs. Mohini Rao** Consumer credit is one of the necessary processes but lending bears

More information

Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu

Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction to Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction Logistics Prerequisites: basics concepts needed in probability and statistics

More information

Introduction To Ensemble Learning

Introduction To Ensemble Learning Educational Series Introduction To Ensemble Learning Dr. Oliver Steinki, CFA, FRM Ziad Mohammad July 2015 What Is Ensemble Learning? In broad terms, ensemble learning is a procedure where multiple learner

More information

Learning Classifiers for Misuse Detection Using a Bag of System Calls Representation

Learning Classifiers for Misuse Detection Using a Bag of System Calls Representation Learning Classifiers for Misuse Detection Using a Bag of System Calls Representation Dae-Ki Kang 1, Doug Fuller 2, and Vasant Honavar 1 1 Artificial Intelligence Lab, Department of Computer Science, Iowa

More information

A Dynamic Integration Algorithm with Ensemble of Classifiers

A Dynamic Integration Algorithm with Ensemble of Classifiers 1 A Dynamic Integration Algorithm with Ensemble of Classifiers Seppo Puuronen 1, Vagan Terziyan 2, Alexey Tsymbal 2 1 University of Jyvaskyla, P.O.Box 35, FIN-40351 Jyvaskyla, Finland sepi@jytko.jyu.fi

More information

Data Mining Techniques Chapter 6: Decision Trees

Data Mining Techniques Chapter 6: Decision Trees Data Mining Techniques Chapter 6: Decision Trees What is a classification decision tree?.......................................... 2 Visualizing decision trees...................................................

More information

Smart Grid Data Analytics for Decision Support

Smart Grid Data Analytics for Decision Support 1 Smart Grid Data Analytics for Decision Support Prakash Ranganathan, Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, USA Prakash.Ranganathan@engr.und.edu, 701-777-4431

More information

Data Mining as Exploratory Data Analysis. Zachary Jones

Data Mining as Exploratory Data Analysis. Zachary Jones Data Mining as Exploratory Data Analysis Zachary Jones The Problem(s) presumptions social systems are complex causal identification is difficult/impossible with many data sources theory not generally predictively

More information

A Survey of Classification Techniques in the Area of Big Data.

A Survey of Classification Techniques in the Area of Big Data. A Survey of Classification Techniques in the Area of Big Data. 1PrafulKoturwar, 2 SheetalGirase, 3 Debajyoti Mukhopadhyay 1Reseach Scholar, Department of Information Technology 2Assistance Professor,Department

More information

Classifiers & Classification

Classifiers & Classification Classifiers & Classification Forsyth & Ponce Computer Vision A Modern Approach chapter 22 Pattern Classification Duda, Hart and Stork School of Computer Science & Statistics Trinity College Dublin Dublin

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

Automatic Web Page Classification

Automatic Web Page Classification Automatic Web Page Classification Yasser Ganjisaffar 84802416 yganjisa@uci.edu 1 Introduction To facilitate user browsing of Web, some websites such as Yahoo! (http://dir.yahoo.com) and Open Directory

More information

Introduction to Learning & Decision Trees

Introduction to Learning & Decision Trees Artificial Intelligence: Representation and Problem Solving 5-38 April 0, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning What is learning? - more than just memorizing

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

Ensemble of Classifiers Based on Association Rule Mining

Ensemble of Classifiers Based on Association Rule Mining Ensemble of Classifiers Based on Association Rule Mining Divya Ramani, Dept. of Computer Engineering, LDRP, KSV, Gandhinagar, Gujarat, 9426786960. Harshita Kanani, Assistant Professor, Dept. of Computer

More information

An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce

An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce by Xuan Liu Thesis submitted to the Faculty of Graduate and Postdoctoral Studies In partial fulfillment of the requirements For

More information

Projektgruppe. Categorization of text documents via classification

Projektgruppe. Categorization of text documents via classification Projektgruppe Steffen Beringer Categorization of text documents via classification 4. Juni 2010 Content Motivation Text categorization Classification in the machine learning Document indexing Construction

More information

Why Ensembles Win Data Mining Competitions

Why Ensembles Win Data Mining Competitions Why Ensembles Win Data Mining Competitions A Predictive Analytics Center of Excellence (PACE) Tech Talk November 14, 2012 Dean Abbott Abbott Analytics, Inc. Blog: http://abbottanalytics.blogspot.com URL:

More information

Evaluation and Credibility. How much should we believe in what was learned?

Evaluation and Credibility. How much should we believe in what was learned? Evaluation and Credibility How much should we believe in what was learned? Outline Introduction Classification with Train, Test, and Validation sets Handling Unbalanced Data; Parameter Tuning Cross-validation

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

More information

ENSEMBLE METHODS FOR CLASSIFIERS

ENSEMBLE METHODS FOR CLASSIFIERS Chapter 45 ENSEMBLE METHODS FOR CLASSIFIERS Lior Rokach Department of Industrial Engineering Tel-Aviv University liorr@eng.tau.ac.il Abstract Keywords: The idea of ensemble methodology is to build a predictive

More information

Lecture Notes for Chapter 4. Introduction to Data Mining

Lecture Notes for Chapter 4. Introduction to Data Mining Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data

More information

Outlier Ensembles. Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598. Keynote, Outlier Detection and Description Workshop, 2013

Outlier Ensembles. Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598. Keynote, Outlier Detection and Description Workshop, 2013 Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598 Outlier Ensembles Keynote, Outlier Detection and Description Workshop, 2013 Based on the ACM SIGKDD Explorations Position Paper: Outlier

More information

Application of Machine Learning to Link Prediction

Application of Machine Learning to Link Prediction Application of Machine Learning to Link Prediction Kyle Julian (kjulian3), Wayne Lu (waynelu) December 6, 6 Introduction Real-world networks evolve over time as new nodes and links are added. Link prediction

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

Using multiple models: Bagging, Boosting, Ensembles, Forests

Using multiple models: Bagging, Boosting, Ensembles, Forests Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or

More information

Predictive Modeling of Titanic Survivors: a Learning Competition

Predictive Modeling of Titanic Survivors: a Learning Competition SAS Analytics Day Predictive Modeling of Titanic Survivors: a Learning Competition Linda Schumacher Problem Introduction On April 15, 1912, the RMS Titanic sank resulting in the loss of 1502 out of 2224

More information

Ensemble Methods for Noise Elimination in Classification Problems

Ensemble Methods for Noise Elimination in Classification Problems Ensemble Methods for Noise Elimination in Classification Problems Sofie Verbaeten and Anneleen Van Assche Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200 A, B-3001 Heverlee,

More information

Meta-learning as intelligent searching through model space

Meta-learning as intelligent searching through model space Meta-learning as intelligent searching through model space Norbert Jankowski & Krzysztof Grąbczewski Department of Informatics Nicolaus Copernicus University Toruń, Poland http://www.is.umk.pl/ norbert@is.umk.pl

More information

UNSUPERVISED MACHINE LEARNING TECHNIQUES IN GENOMICS

UNSUPERVISED MACHINE LEARNING TECHNIQUES IN GENOMICS UNSUPERVISED MACHINE LEARNING TECHNIQUES IN GENOMICS Dwijesh C. Mishra I.A.S.R.I., Library Avenue, New Delhi-110 012 dcmishra@iasri.res.in What is Learning? "Learning denotes changes in a system that enable

More information

Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

More information

Meta-Learning in Distributed Data Mining Systems: Issues and Approaches

Meta-Learning in Distributed Data Mining Systems: Issues and Approaches Meta-Learning in Distributed Data Mining Systems: Issues and Approaches Andreas L. Prodromidis Computer Science Department Columbia University New York, NY 10027 andreas@cs.columbia.edu Philip K. Chan

More information

Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News

Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Sushilkumar Kalmegh Associate Professor, Department of Computer Science, Sant Gadge Baba Amravati

More information

The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2

The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2 1 School of

More information

Classification with Decision Trees

Classification with Decision Trees Classification with Decision Trees Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong 1 / 24 Y Tao Classification with Decision Trees In this lecture, we will discuss

More information

BIDM Project. Predicting the contract type for IT/ITES outsourcing contracts

BIDM Project. Predicting the contract type for IT/ITES outsourcing contracts BIDM Project Predicting the contract type for IT/ITES outsourcing contracts N a n d i n i G o v i n d a r a j a n ( 6 1 2 1 0 5 5 6 ) The authors believe that data modelling can be used to predict if an

More information

The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner

The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner Paper 3361-2015 The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner Narmada Deve Panneerselvam, Spears School of Business, Oklahoma State University, Stillwater,

More information

Meta-learning in distributed data mining systems: Issues and approaches

Meta-learning in distributed data mining systems: Issues and approaches Meta-learning in distributed data mining systems: Issues and approaches Andreas L. Prodromidis Computer Science Department Columbia University New York, NY 10027 andreas@cs.columbia.edu Salvatore J. Stolfo

More information

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century Nora Galambos, PhD Senior Data Scientist Office of Institutional Research, Planning & Effectiveness Stony Brook University AIRPO

More information

Predictive Modeling with The R CARET Package

Predictive Modeling with The R CARET Package Predictive Modeling with The R CARET Package Matthew A. Lanham, CAP (lanham@vt.edu) Doctoral Candidate Department of Business Information Technology MatthewALanham.com R is a software environment for data

More information

Leveraging Ensemble Models in SAS Enterprise Miner

Leveraging Ensemble Models in SAS Enterprise Miner ABSTRACT Paper SAS133-2014 Leveraging Ensemble Models in SAS Enterprise Miner Miguel Maldonado, Jared Dean, Wendy Czika, and Susan Haller SAS Institute Inc. Ensemble models combine two or more models to

More information

Studying Auto Insurance Data

Studying Auto Insurance Data Studying Auto Insurance Data Ashutosh Nandeshwar February 23, 2010 1 Introduction To study auto insurance data using traditional and non-traditional tools, I downloaded a well-studied data from http://www.statsci.org/data/general/motorins.

More information