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Machine Learning for Data Mining 1 Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer • Classification and Regression • Clustering • Association Rules • Attribute Selection • Data Visualization The Experimenter The Knowledge Flow GUI Conclusions Machine Learning with WEKA 2/4/2004 University of Waikato 2 WEKA: the bird Copyright: Martin Kramer ([email protected])

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  • Machine Learning for Data Mining 1

    Department of Computer Science, University of Waikato, New Zealand

    Eibe Frank

    � WEKA: A Machine Learning Toolkit

    � The Explorer• Classification and

    Regression

    • Clustering

    • Association Rules

    • Attribute Selection

    • Data Visualization

    � The Experimenter

    � The Knowledge Flow GUI

    � Conclusions

    Machine Learning with WEKA

    2/4/2004 University of Waikato 2

    WEKA: the bird

    Copyright: Martin Kramer ([email protected])

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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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    WEKA: versions

    � There are several versions of WEKA:� WEKA 3.0: “book version” compatible with

    description in data mining book� WEKA 3.2: “GUI version” adds graphical user

    interfaces (book version is command-line only)� WEKA 3.3: “development version” with lots of

    improvements

    � This talk is based on the latest snapshot of WEKA 3.3 (soon to be WEKA 3.4)

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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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    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

    � 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 an implementation of the Apriori algorithm 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

    � New graphical user interface for WEKA� 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

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    Conclusion: try it yourself!

    � WEKA is available athttp://www.cs.waikato.ac.nz/ml/weka

    � Also has a list of projects based on WEKA� WEKA contributors:

    Abdelaziz Mahoui, Alexander K. Seewald, Ashraf M. Kibriya, BernhardPfahringer , 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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