Tree Algorithms in Data Mining: Comparison of rpart and RWeka... and Beyond
|
|
|
- Britton Flowers
- 7 years ago
- Views:
Transcription
1 Tree Algorithms in Data Mining: Comparison of rpart and RWeka... and Beyond Achim Zeileis
2 Motivation For publishing new tree algorithms, benchmarks against established methods are necessary. When developing the tools in party, we benchmarked against rpart, the open-source implementation of CART. Statistical journals were usually happy with that. Usual comment from machine learners: You have to benchmark against C4.5, it s much better than CART! Quinlan provided source code for C4.5, but not with a license that would allow usage. Weka had an open-source Java implementation, but hard to access from R. When we developed RWeka, we finally were able to set up some benchmark with CART and C4.5 within R.
3 Tree algorithms CART/RPart rpart: Classification and regression trees Breiman, Friedman, Olshen, Stone Cross-validation-based cost-complexity pruning: RPart0: Best prediction error. RPart1: Highest complexity parameter within 1 standard error. C4.5/J4.8 RWeka: C4.5 Quinlan, Determine size by confidence threshold C and minimal leaf size M: J4.8: Standard heuristics C = 0.25, M = 2. J4.8cv: Cross-validation for C = 0.01,..., 0.5, M = 2,..., 20. QUEST LohTools: Quick, unbiased and efficient statistical trees Loh, Shih Popularized concept of unbiased recursive partitioning in statistics. Hand-crafted convenience interface to original binaries. CTree party: Conditional inference trees Hothorn, Hornik, Zeileis Unbiased recursive partitioning based on permutation tests.
4 UCI data sets mlbench Data set # of obs. # of cat. inputs # of num. inputs breast cancer chess circle credit heart hepatitis house votes ionosphere liver Pima Indians diabetes promotergene ringnorm sonar spirals threenorm tictactoe titanic twonorm
5 Analysis 6 tree algorithms. 18 data sets. 500 bootstrap samples for each combination. Performance measure: Out-of-bag misclassification rate. Complexity measure: Number of splits + number of leafs. Individual results: Simultaneous pairwise confidence intervals Tukey all-pair comparisons. Aggregated results: Bradley-Terry model Alternatively: median linear consensus ranking,....
6 Individual results: Pima Indian diabetes J4.8cv J4.8 RPart0 J4.8 RPart1 J4.8 QUEST J4.8 CTree J4.8 RPart0 J4.8cv RPart1 J4.8cv QUEST J4.8cv CTree J4.8cv RPart1 RPart0 QUEST RPart0 CTree RPart0 QUEST RPart1 CTree RPart1 CTree QUEST Misclassification difference in percent
7 Individual results: Pima Indian diabetes J4.8cv J4.8 RPart0 J4.8 RPart1 J4.8 QUEST J4.8 CTree J4.8 RPart0 J4.8cv RPart1 J4.8cv QUEST J4.8cv CTree J4.8cv RPart1 RPart0 QUEST RPart0 CTree RPart0 QUEST RPart1 CTree RPart1 CTree QUEST Complexity difference
8 Individual results: Breast cancer J4.8cv J4.8 RPart0 J4.8 RPart1 J4.8 QUEST J4.8 CTree J4.8 RPart0 J4.8cv RPart1 J4.8cv QUEST J4.8cv CTree J4.8cv RPart1 RPart0 QUEST RPart0 CTree RPart0 QUEST RPart1 CTree RPart1 CTree QUEST Misclassification difference in percent
9 Individual results: Breast cancer J4.8cv J4.8 RPart0 J4.8 RPart1 J4.8 QUEST J4.8 CTree J4.8 RPart0 J4.8cv RPart1 J4.8cv QUEST J4.8cv CTree J4.8cv RPart1 RPart0 QUEST RPart0 CTree RPart0 QUEST RPart1 CTree RPart1 CTree QUEST Complexity difference
10 Aggregated results: Misclassification Worth parameters J4.8 J4.8cv RPart0 RPart1 QUEST CTree Objects
11 Aggregated results: Complexity Worth parameters J4.8 J4.8cv RPart0 RPart1 QUEST CTree Objects
12 Summary No clear preference between CART/RPart and C4.5/J4.8. Other tree algorithms perform similarly well. Cross-validated trees perform better than their counterparts. 1-standard error rule does not seem to be supported. And now for something different: Before: Pairwise comparisons of tree algorithms. Now: Tree algorithm for pairwise comparison data.
13 Model-based recursive partitioning Generic algorithm: 1 Fit parametric model for Y. 2 Assess stability of the model parameters over each splitting variable Z j. 3 Split sample along the Z j with strongest association: Choose breakpoint with highest improvement of the model fit. 4 Repeat steps 1 3 recursively in the subsamples until no more significant instabilities. Application: Use Bradley-Terry models in step 1. Implementation: psychotree on R-Forge.
14 Germany s Next Topmodel Study at Department of Psychology, Universität Tübingen. 192 subjects rated the attractiveness of candidates in 2nd season of Germany s Next Topmodel. 6 finalists: Barbara Meier, Anni Wendler, Hana Nitsche, Fiona Erdmann, Mandy Graff and Anja Platzer. Pairwise comparison with forced choice. Subject covariates: Gender, age, questions about interest in the show.
15 Germany s Next Topmodel
16 Germany s Next Topmodel 1 age p < q2 p = yes no 52 > 52 4 gender p = male female 0.5 Node 3 n = Node 5 n = Node 6 n = Node 7 n = B Ann H F M Anj B Ann H F M Anj B Ann H F M Anj B Ann H F M Anj
17 References Hothorn T, Leisch F, Zeileis A, Hornik K The Design and Analysis of Benchmark Experiments. Journal of Computational and Graphical Statistics, 143, doi: / x59630 Schauerhuber M, Zeileis A, Meyer D Benchmarking Open-Source Tree Learners in R/RWeka. In C Preisach, H Burkhardt, L Schmidt-Thieme, R Decker eds., Data Analysis, Machine Learning and Applications Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.v., Albert-Ludwigs-Universität Freiburg, March 7 9, pp Hornik K, Buchta C, Zeileis A Open-Source Machine Learning: R Meets Weka. Computational Statistics, 242, doi: /s Strobl C, Wickelmaier F, Zeileis A Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning. Technical Report 54, Department of Statistics, Ludwig-Maximilians-Universität München. URL
Benchmarking Open-Source Tree Learners in R/RWeka
Benchmarking Open-Source Tree Learners in R/RWeka Michael Schauerhuber 1, Achim Zeileis 1, David Meyer 2, Kurt Hornik 1 Department of Statistics and Mathematics 1 Institute for Management Information Systems
Open-Source Machine Learning: R Meets Weka
Open-Source Machine Learning: R Meets Weka Kurt Hornik, Christian Buchta, Michael Schauerhuber, David Meyer, Achim Zeileis http://statmath.wu-wien.ac.at/ zeileis/ Weka? Weka is not only a flightless endemic
partykit: A Toolkit for Recursive Partytioning
partykit: A Toolkit for Recursive Partytioning Achim Zeileis, Torsten Hothorn http://eeecon.uibk.ac.at/~zeileis/ Overview Status quo: R software for tree models New package: partykit Unified infrastructure
Model-Based Recursive Partitioning for Detecting Interaction Effects in Subgroups
Model-Based Recursive Partitioning for Detecting Interaction Effects in Subgroups Achim Zeileis, Torsten Hothorn, Kurt Hornik http://eeecon.uibk.ac.at/~zeileis/ Overview Motivation: Trees, leaves, and
Model-based Recursive Partitioning
Model-based Recursive Partitioning Achim Zeileis Wirtschaftsuniversität Wien Torsten Hothorn Ludwig-Maximilians-Universität München Kurt Hornik Wirtschaftsuniversität Wien Abstract Recursive partitioning
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
Classification tree analysis using TARGET
Computational Statistics & Data Analysis 52 (2008) 1362 1372 www.elsevier.com/locate/csda Classification tree analysis using TARGET J. Brian Gray a,, Guangzhe Fan b a Department of Information Systems,
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
REPORT DOCUMENTATION PAGE
REPORT DOCUMENTATION PAGE Form Approved OMB NO. 0704-0188 Public Reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions,
ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA
ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA D.Lavanya 1 and Dr.K.Usha Rani 2 1 Research Scholar, Department of Computer Science, Sree Padmavathi Mahila Visvavidyalayam, Tirupati, Andhra Pradesh,
How To Find Seasonal Differences Between The Two Programs Of The Labor Market
Using Data Mining to Explore Seasonal Differences Between the U.S. Current Employment Statistics Survey and the Quarterly Census of Employment and Wages October 2009 G. Erkens BLS G. Erkens, Bureau of
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
Applied Multivariate Analysis - Big data analytics
Applied Multivariate Analysis - Big data analytics Nathalie Villa-Vialaneix nathalie.villa@toulouse.inra.fr http://www.nathalievilla.org M1 in Economics and Economics and Statistics Toulouse School of
The Design and Analysis of Benchmark Experiments
The Design and Analysis of Benchmark Experiments Torsten Hothorn Friedrich-Alexander-Universität Erlangen-Nürnberg Friedrich Leisch Technische Universität Wien Kurt Hornik Wirtschaftsuniversität Wien Achim
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 &
ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS
ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS Abstract D.Lavanya * Department of Computer Science, Sri Padmavathi Mahila University Tirupati, Andhra Pradesh, 517501, India lav_dlr@yahoo.com
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
R: A Free Software Project in Statistical Computing
R: A Free Software Project in Statistical Computing Achim Zeileis http://statmath.wu-wien.ac.at/ zeileis/ Achim.Zeileis@R-project.org Overview A short introduction to some letters of interest R, S, Z Statistics
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
Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms
Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms Yin Zhao School of Mathematical Sciences Universiti Sains Malaysia (USM) Penang, Malaysia Yahya
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
6 Classification and Regression Trees, 7 Bagging, and Boosting
hs24 v.2004/01/03 Prn:23/02/2005; 14:41 F:hs24011.tex; VTEX/ES p. 1 1 Handbook of Statistics, Vol. 24 ISSN: 0169-7161 2005 Elsevier B.V. All rights reserved. DOI 10.1016/S0169-7161(04)24011-1 1 6 Classification
Scaling Up the Accuracy of Naive-Bayes Classiers: a Decision-Tree Hybrid. Ron Kohavi. Silicon Graphics, Inc. 2011 N. Shoreline Blvd. ronnyk@sgi.
Scaling Up the Accuracy of Classiers: a Decision-Tree Hybrid Ron Kohavi Data Mining and Visualization Silicon Graphics, Inc. 2011 N. Shoreline Blvd Mountain View, CA 94043-1389 ronnyk@sgi.com Abstract
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
Open-Source Machine Learning: R Meets Weka
Open-Source Machine Learning: R Meets Weka Kurt Hornik Christian Buchta Achim Zeileis Weka? Weka is not only a flightless endemic bird of New Zealand (Gallirallus australis, picture from Wekapedia) but
Decision Tree Learning on Very Large Data Sets
Decision Tree Learning on Very Large Data Sets Lawrence O. Hall Nitesh Chawla and Kevin W. Bowyer Department of Computer Science and Engineering ENB 8 University of South Florida 4202 E. Fowler Ave. Tampa
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
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
Feature vs. Classifier Fusion for Predictive Data Mining a Case Study in Pesticide Classification
Feature vs. Classifier Fusion for Predictive Data Mining a Case Study in Pesticide Classification Henrik Boström School of Humanities and Informatics University of Skövde P.O. Box 408, SE-541 28 Skövde
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
Data mining on the EPIRARE survey data
Deliverable D1.5 Data mining on the EPIRARE survey data A. Coi 1, M. Santoro 2, M. Lipucci 2, A.M. Bianucci 1, F. Bianchi 2 1 Department of Pharmacy, Unit of Research of Bioinformatic and Computational
Logistic Model Trees
Logistic Model Trees Niels Landwehr 1,2, Mark Hall 2, and Eibe Frank 2 1 Department of Computer Science University of Freiburg Freiburg, Germany landwehr@informatik.uni-freiburg.de 2 Department of Computer
Chapter 12 Bagging and Random Forests
Chapter 12 Bagging and Random Forests Xiaogang Su Department of Statistics and Actuarial Science University of Central Florida - 1 - Outline A brief introduction to the bootstrap Bagging: basic concepts
Roulette Sampling for Cost-Sensitive Learning
Roulette Sampling for Cost-Sensitive Learning Victor S. Sheng and Charles X. Ling Department of Computer Science, University of Western Ontario, London, Ontario, Canada N6A 5B7 {ssheng,cling}@csd.uwo.ca
Package NHEMOtree. February 19, 2015
Type Package Package NHEMOtree February 19, 2015 Title Non-hierarchical evolutionary multi-objective tree learner to perform cost-sensitive classification Depends partykit, emoa, sets, rpart Version 1.0
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
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
Monitoring Structural Change in Dynamic Econometric Models
Monitoring Structural Change in Dynamic Econometric Models Achim Zeileis Friedrich Leisch Christian Kleiber Kurt Hornik http://www.ci.tuwien.ac.at/~zeileis/ Contents Model frame Generalized fluctuation
CLUSTERING AND PREDICTIVE MODELING: AN ENSEMBLE APPROACH
CLUSTERING AND PREDICTIVE MODELING: AN ENSEMBLE APPROACH Except where reference is made to the work of others, the work described in this thesis is my own or was done in collaboration with my advisory
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
M1 in Economics and Economics and Statistics Applied multivariate Analysis - Big data analytics Worksheet 3 - Random Forest
Nathalie Villa-Vialaneix Année 2014/2015 M1 in Economics and Economics and Statistics Applied multivariate Analysis - Big data analytics Worksheet 3 - Random Forest This worksheet s aim is to learn how
An Overview and Evaluation of Decision Tree Methodology
An Overview and Evaluation of Decision Tree Methodology ASA Quality and Productivity Conference Terri Moore Motorola Austin, TX terri.moore@motorola.com Carole Jesse Cargill, Inc. Wayzata, MN carole_jesse@cargill.com
Introducing diversity among the models of multi-label classification ensemble
Introducing diversity among the models of multi-label classification ensemble Lena Chekina, Lior Rokach and Bracha Shapira Ben-Gurion University of the Negev Dept. of Information Systems Engineering and
TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM
TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam
2 Decision tree + Cross-validation with R (package rpart)
1 Subject Using cross-validation for the performance evaluation of decision trees with R, KNIME and RAPIDMINER. This paper takes one of our old study on the implementation of cross-validation for assessing
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
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.
Classification and regression trees
Overview Wei-Yin Loh are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model
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
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
A Comparison of Decision Tree and Logistic Regression Model Xianzhe Chen, North Dakota State University, Fargo, ND
Paper D02-2009 A Comparison of Decision Tree and Logistic Regression Model Xianzhe Chen, North Dakota State University, Fargo, ND ABSTRACT This paper applies a decision tree model and logistic regression
Selecting Data Mining Model for Web Advertising in Virtual Communities
Selecting Data Mining for Web Advertising in Virtual Communities Jerzy Surma Faculty of Business Administration Warsaw School of Economics Warsaw, Poland e-mail: jerzy.surma@gmail.com Mariusz Łapczyński
TACKLING SIMPSON'S PARADOX IN BIG DATA USING CLASSIFICATION & REGRESSION TREES
TACKLING SIMPSON'S PARADOX IN BIG DATA USING CLASSIFICATION & REGRESSION TREES Research in Progress Shmueli, Galit, Indian School of Business, Hyderabad, India, galit_shmueli@isb.edu Yahav, Inbal, Bar
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
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
Model Trees for Classification of Hybrid Data Types
Model Trees for Classification of Hybrid Data Types Hsing-Kuo Pao, Shou-Chih Chang, and Yuh-Jye Lee Dept. of Computer Science & Information Engineering, National Taiwan University of Science & Technology,
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
A Methodology for Predictive Failure Detection in Semiconductor Fabrication
A Methodology for Predictive Failure Detection in Semiconductor Fabrication Peter Scheibelhofer (TU Graz) Dietmar Gleispach, Günter Hayderer (austriamicrosystems AG) 09-09-2011 Peter Scheibelhofer (TU
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,
testo dello schema Secondo livello Terzo livello Quarto livello Quinto livello
Extracting Knowledge from Biomedical Data through Logic Learning Machines and Rulex Marco Muselli Institute of Electronics, Computer and Telecommunication Engineering National Research Council of Italy,
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
Fortgeschrittene Computerintensive Methoden
Fortgeschrittene Computerintensive Methoden Einheit 3: mlr - Machine Learning in R Bernd Bischl Matthias Schmid, Manuel Eugster, Bettina Grün, Friedrich Leisch Institut für Statistik LMU München SoSe 2014
harpreet@utdallas.edu, {ram.gopal, xinxin.li}@business.uconn.edu
Risk and Return of Investments in Online Peer-to-Peer Lending (Extended Abstract) Harpreet Singh a, Ram Gopal b, Xinxin Li b a School of Management, University of Texas at Dallas, Richardson, Texas 75083-0688
Data Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.7 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Linear Regression Other Regression Models References Introduction Introduction Numerical prediction is
Fortgeschrittene Computerintensive Methoden
Fortgeschrittene Computerintensive Methoden Einheit 5: mlr - Machine Learning in R Bernd Bischl Matthias Schmid, Manuel Eugster, Bettina Grün, Friedrich Leisch Institut für Statistik LMU München SoSe 2015
Statistical Data Mining. Practical Assignment 3 Discriminant Analysis and Decision Trees
Statistical Data Mining Practical Assignment 3 Discriminant Analysis and Decision Trees In this practical we discuss linear and quadratic discriminant analysis and tree-based classification techniques.
Selecting Machine Learning Algorithms using Regression Models
Selecting Machine Learning Algorithms using Regression Models Tri Doan Department of Computer Science University of Colorado Colorado Springs tdoan@uccs.edu Jugal Kalita Department of Computer Science
Event driven trading new studies on innovative way. of trading in Forex market. Michał Osmoła INIME live 23 February 2016
Event driven trading new studies on innovative way of trading in Forex market Michał Osmoła INIME live 23 February 2016 Forex market From Wikipedia: The foreign exchange market (Forex, FX, or currency
Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation
Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation James K. Kimotho, Christoph Sondermann-Woelke, Tobias Meyer, and Walter Sextro Department
Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets
Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification
Application of Data mining in predicting cell phones Subscribers Behavior Employing the Contact pattern
Application of Data mining in predicting cell phones Subscribers Behavior Employing the Contact pattern Rahman Mansouri Faculty of Postgraduate Studies Department of Computer University of Najaf Abad Islamic
International Journal of Computer Sciences and Engineering Open Access
International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Issue-2 E-ISSN: 2347-2693 Enhancing Web Learning System Using Multimedia and Regression Algorithm J. Mary
A Review of Missing Data Treatment Methods
A Review of Missing Data Treatment Methods Liu Peng, Lei Lei Department of Information Systems, Shanghai University of Finance and Economics, Shanghai, 200433, P.R. China ABSTRACT Missing data is a common
Beating the MLB Moneyline
Beating the MLB Moneyline Leland Chen llxchen@stanford.edu Andrew He andu@stanford.edu 1 Abstract Sports forecasting is a challenging task that has similarities to stock market prediction, requiring time-series
Guide to Credit Scoring in R
Credit Scoring in R 1 of 45 Guide to Credit Scoring in R By DS (ds5j@excite.com) (Interdisciplinary Independent Scholar with 9+ years experience in risk management) Summary To date Sept 23 2009, as Ross
THE RISE OF THE BIG DATA: WHY SHOULD STATISTICIANS EMBRACE COLLABORATIONS WITH COMPUTER SCIENTISTS XIAO CHENG. (Under the Direction of Jeongyoun Ahn)
THE RISE OF THE BIG DATA: WHY SHOULD STATISTICIANS EMBRACE COLLABORATIONS WITH COMPUTER SCIENTISTS by XIAO CHENG (Under the Direction of Jeongyoun Ahn) ABSTRACT Big Data has been the new trend in businesses.
A Property & Casualty Insurance Predictive Modeling Process in SAS
Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing
Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal
Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether
Defining the Study Population for an Observational Study to Ensure Sufficient Overlap: A Tree Approach
Statistics in the Biosciences manuscript No. (will be inserted by the editor) Defining the Study Population for an Observational Study to Ensure Sufficient Overlap: A Tree Approach Mikhail Traskin Dylan
DATA MINING METHODS WITH TREES
DATA MINING METHODS WITH TREES Marta Žambochová 1. Introduction The contemporary world is characterized by the explosion of an enormous volume of data deposited into databases. Sharp competition contributes
Decision Trees. Andrew W. Moore Professor School of Computer Science Carnegie Mellon University. www.cs.cmu.edu/~awm awm@cs.cmu.
Decision Trees Andrew W. Moore Professor School of Computer Science Carnegie Mellon University www.cs.cmu.edu/~awm awm@cs.cmu.edu 42-268-7599 Copyright Andrew W. Moore Slide Decision Trees Decision trees
KNIME TUTORIAL. Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it
KNIME TUTORIAL Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it Outline Introduction on KNIME KNIME components Exercise: Market Basket Analysis Exercise: Customer Segmentation Exercise:
Classification and Regression Tree Analysis
Technical Report No. 1 May 8, 2014 Classification and Regression Tree Analysis Jake Morgan jakem@bu.edu This paper was published in fulfillment of the requirements for PM931 Directed Study in Health Policy
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
THE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING. Dan Steinberg and N. Scott Cardell
THE HYBID CAT-LOGIT MODEL IN CLASSIFICATION AND DATA MINING Introduction Dan Steinberg and N. Scott Cardell Most data-mining projects involve classification problems assigning objects to classes whether
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:
Predicting required bandwidth for educational institutes using prediction techniques in data mining (Case Study: Qom Payame Noor University)
260 IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.6, June 2011 Predicting required bandwidth for educational institutes using prediction techniques in data mining (Case
Mauro Sousa Marta Mattoso Nelson Ebecken. and these techniques often repeatedly scan the. entire set. A solution that has been used for a
Data Mining on Parallel Database Systems Mauro Sousa Marta Mattoso Nelson Ebecken COPPEèUFRJ - Federal University of Rio de Janeiro P.O. Box 68511, Rio de Janeiro, RJ, Brazil, 21945-970 Fax: +55 21 2906626
Efficiency in Software Development Projects
Efficiency in Software Development Projects Aneesh Chinubhai Dharmsinh Desai University aneeshchinubhai@gmail.com Abstract A number of different factors are thought to influence the efficiency of the software
Handling missing data in large data sets. Agostino Di Ciaccio Dept. of Statistics University of Rome La Sapienza
Handling missing data in large data sets Agostino Di Ciaccio Dept. of Statistics University of Rome La Sapienza The problem Often in official statistics we have large data sets with many variables and
Random Forests for Regression and Classification. Adele Cutler Utah State University
Random Forests for Regression and Classification Adele Cutler Utah State University September 5-7, 2 Ovronnaz, Switzerland Leo Breiman, 928-25 954: PhD Berkeley (mathematics) 96-967: UCLA (mathematics)
Big Data Analysis. Rajen D. Shah (Statistical Laboratory, University of Cambridge) joint work with Nicolai Meinshausen (Seminar für Statistik, ETH
Big Data Analysis Rajen D Shah (Statistical Laboratory, University of Cambridge) joint work with Nicolai Meinshausen (Seminar für Statistik, ETH Zürich) University of Cambridge Mathematical Sciences Showcase
Chapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
Scoring the Data Using Association Rules
Scoring the Data Using Association Rules Bing Liu, Yiming Ma, and Ching Kian Wong School of Computing National University of Singapore 3 Science Drive 2, Singapore 117543 {liub, maym, wongck}@comp.nus.edu.sg
An Experimental Study on Rotation Forest Ensembles
An Experimental Study on Rotation Forest Ensembles Ludmila I. Kuncheva 1 and Juan J. Rodríguez 2 1 School of Electronics and Computer Science, University of Wales, Bangor, UK l.i.kuncheva@bangor.ac.uk
Big Data Decision Trees with R
REVOLUTION ANALYTICS WHITE PAPER Big Data Decision Trees with R By Richard Calaway, Lee Edlefsen, and Lixin Gong Fast, Scalable, Distributable Decision Trees Revolution Analytics RevoScaleR package provides
Identification of User Patterns in Social Networks by Data Mining Techniques: Facebook Case
Identification of User Patterns in Social Networks by Data Mining Techniques: Facebook Case A. Selman Bozkır 1, S. Güzin Mazman 2, and Ebru Akçapınar Sezer 1 1 Hacettepe University, Department of Computer
Lecture 10: Regression Trees
Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,
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
EMPIRICAL STUDY ON SELECTION OF TEAM MEMBERS FOR SOFTWARE PROJECTS DATA MINING APPROACH
EMPIRICAL STUDY ON SELECTION OF TEAM MEMBERS FOR SOFTWARE PROJECTS DATA MINING APPROACH SANGITA GUPTA 1, SUMA. V. 2 1 Jain University, Bangalore 2 Dayanada Sagar Institute, Bangalore, India Abstract- One