Department of Computer Science, University of Waikato, New Zealand

Bernhard Pfahringer(based on material by Eibe Frank, Mark

Hall, and Peter Reutemann)

WEKA: A Machine Learning Toolkit

The Explorer• Classification and

Regression• Clustering• Association Rules• Attribute Selection• Data Visualization

The Experimenter The Knowledge

Flow GUI Other Utilities Conclusions

Machine Learning with WEKA

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WEKA: the bird

Copyright: Martin Kramer (mkramer@wxs.nl)

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

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WEKA: the software 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

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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

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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 AAndrew’s RRidiculous FFile FFormat 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

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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 (JAJAva WWeka SSystem)

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)

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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)

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@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}

@data63,male,typ_angina,233,no,not_present67,male,asympt,286,yes,present67,male,asympt,229,yes,present38,female,non_anginal,?,no,not_present...

WEKA only deals with “flat” files

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@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}

@data63,male,typ_angina,233,no,not_present67,male,asympt,286,yes,present67,male,asympt,229,yes,present38,female,non_anginal,?,no,not_present...

WEKA only deals with “flat” files

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java -jar weka.jar

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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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Explorer: building “classifiers” 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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Explorer: clustering data 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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Explorer: finding associations 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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Explorer: attribute selection 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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Explorer: data visualization 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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Performing experiments 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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The Knowledge Flow GUI

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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Sourceforge.net – Downloads

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Sourceforge.net – Web Traffic

WekaWiki launched – 05/2005

WekaDoc Wiki introduced – 12/2005

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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

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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

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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!)

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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