Machine Learning with WEKA

WEKA: A Machine Machine Learning with Learning Toolkit WEKA The Explorer • Classification and Regression

• Clustering

Bernhard Pfahringer • Association Rules (based on material by Eibe Frank, Mark • Attribute Selection Hall, and Peter Reutemann) • Data Visualization

Department of Computer Science, The Experimenter University of Waikato, New Zealand The Knowledge Flow GUI

Other Utilities

Conclusions WEKA: the bird

The Weka or woodhen (Gallirallus australis) is an endemic bird of New Zealand. (Source: WikiPedia)

Machine learning/data mining software written in Java (distributed under the GNU Public License)

Used for research, education, and applications

Complements “Data Mining” by Witten & Frank

Main features: Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods Graphical user interfaces (incl. data visualization) Environment for comparing learning algorithms

5/14/2007 University of Waikato 3 History

Project funded by the NZ government since 1993

Develop state-of-the art workbench of data mining tools Explore fielded applications Develop new fundamental methods

5/14/2007 University of Waikato 4 History (2)

Late 1992 - funding was applied for by Ian Witten 1993 - development of the interface and infrastructure WEKA acronym coined by Geoff Holmes WEKA’s file format “ARFF” was created by Andrew Donkin ARFF was rumored to stand for Andrew’s Ridiculous File Format Sometime in 1994 - first internal release of WEKA TCL/TK user interface + learning algorithms written mostly in C Very much beta software Changes for the b1 release included (among others): “Ambiguous and Unsupported menu commands removed.” “Crashing processes handled (in most cases :-)”

October 1996 - first public release: WEKA 2.1

5/14/2007 University of Waikato 5 History (3)

July 1997 - WEKA 2.2 Schemes: 1R, T2, K*, M5, M5Class, IB1-4, FOIL, PEBLS, support for C5 Included a facility (based on Unix makefiles) for configuring and running large scale experiments Early 1997 - decision was made to rewrite WEKA in Java Originated from code written by Eibe Frank for his PhD Originally codenamed JAWS (JAva Weka System) May 1998 - WEKA 2.3 Last release of the TCL/TK-based system Mid 1999 - WEKA 3 (100% Java) released Version to complement the Data Mining book Development version (including GUI)

5/14/2007 University of Waikato 6 The GUI back then… TCL/TK interface of Weka 2.1

5/14/2007 University of Waikato 7 WEKA: versions

There are several versions of WEKA: WEKA 3.4: “book version” compatible with description in data mining book WEKA 3.5.5: “development version” with lots of improvements

This talk is based on a nightly snapshot of WEKA 3.5.5 (12-Feb-2007)

5/14/2007 University of Waikato 8 WEKA only deals with “flat” files

@relation heart-disease-simplified

@attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present}

@data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ...

5/14/2007 University of Waikato 9 WEKA only deals with “flat” files

@relation heart-disease-simplified

@data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ...

5/14/2007 University of Waikato 10 java weka.gui.GUIChooser

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5/14/2007 University of Waikato 14 Explorer: pre-processing the data

Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary

Data can also be read from a URL or from an SQL database (using JDBC)

Pre-processing tools in WEKA are called “filters”

WEKA contains filters for: Discretization, normalization, resampling, attribute selection, transforming and combining attributes, …

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Classifiers in WEKA are models for predicting nominal or numeric quantities

Implemented learning schemes include: Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, …

“Meta”-classifiers include: Bagging, boosting, stacking, error-correcting output codes, locally weighted learning, …

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WEKA contains “clusterers” for finding groups of similar instances in a dataset

Some implemented schemes are: k-Means, EM, Cobweb, X-means, FarthestFirst

Clusters can be visualized and compared to “true” clusters (if given)

Evaluation based on loglikelihood if clustering scheme produces a probability distribution

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WEKA contains the Apriori algorithm (among others) for learning association rules Works only with discrete data

Can identify statistical dependencies between groups of attributes: milk, butter ⇒ bread, eggs (with confidence 0.9 and support 2000)

Apriori can compute all rules that have a given minimum support and exceed a given confidence

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Panel that can be used to investigate which (subsets of) attributes are the most predictive ones

Attribute selection methods contain two parts: A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking An evaluation method: correlation-based, wrapper, information gain, chi-squared, …

Very flexible: WEKA allows (almost) arbitrary combinations of these two

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Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem

WEKA can visualize single attributes (1-d) and pairs of attributes (2-d) To do: rotating 3-d visualizations (Xgobi-style)

Color-coded class values

“Jitter” option to deal with nominal attributes (and to detect “hidden” data points)

“Zoom-in” function

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Experimenter makes it easy to compare the performance of different learning schemes

For classification and regression problems

Results can be written into file or database

Evaluation options: cross-validation, learning curve, hold-out

Can also iterate over different parameter settings

Significance-testing built in!

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Java-Beans-based interface for setting up and running machine learning experiments Data sources, classifiers, etc. are beans and can be connected graphically Data “flows” through components: e.g., “data source” -> “filter” -> “classifier” -> “evaluator” Layouts can be saved and loaded again later cf. Clementine ™

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5/14/2007 University of Waikato 152 Sourceforge.net – Web Traffic

WekaDoc Wiki introduced – 12/2005

WekaWiki launched – 05/2005

5/14/2007 University of Waikato 153 Projects based on WEKA

45 projects currently (30/01/07) listed on the WekaWiki Incorporate/wrap WEKA GRB Tool Shed - a tool to aid gamma ray burst research YALE - facility for large scale ML experiments GATE - NLP workbench with a WEKA interface Judge - document clustering and classification RWeka - an R interface to Weka Extend/modify WEKA BioWeka - extension library for knowledge discovery in biology WekaMetal - meta learning extension to WEKA Weka-Parallel - parallel processing for WEKA Grid Weka - grid computing using WEKA Weka-CG - computational genetics tool library

5/14/2007 University of Waikato 154 WEKA and PENTAHO

Pentaho – The leader in Open Source Business Intelligence (BI) September 2006 – Pentaho acquires the Weka project (exclusive license and SF.net page) Weka will be used/integrated as data mining component in their BI suite Weka will be still available as GPL open source software Most likely to evolve 2 editions: Community edition BI oriented edition

5/14/2007 University of Waikato 155 Limitations of WEKA

Traditional algorithms need to have all data in main memory

==> big datasets are an issue

Solution: Incremental schemes Stream algorithms MOA “MMassive OOnline AAnalysis” (not only a flightless bird, but also extinct!)

5/14/2007 University of Waikato 156 Conclusion: try it yourself!

WEKA is available at http://www.cs.waikato.ac.nz/ml/weka Also has a list of projects based on WEKA (probably incomplete list of) WEKA contributors: Abdelaziz Mahoui, Alexander K. Seewald, Ashraf M. Kibriya, Bernhard Pfahringer, Brent Martin, Peter Flach, Eibe Frank, Gabi Schmidberger, Ian H. Witten, J. Lindgren, Janice Boughton, Jason Wells, Len Trigg, Lucio de Souza Coelho, Malcolm Ware, Mark Hall, Remco Bouckaert, Richard Kirkby, Shane Butler, Shane Legg, Stuart Inglis, Sylvain Roy, Tony Voyle, Xin Xu, Yong Wang, Zhihai Wang

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