The Weka machine learning workbench provides a general purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it

Frank, Eibe

Hall, Mark A.

Holmes, Geoffrey

Trigg, Leonard E.

Witten, Ian H.

English

Name not available

Data mining in bioinformatics using Weka

Research Commons@Waikato

Frank et al.Data Mining in Bioinformatics using WekaEibe Frank1,∗, Mark Hall1, Len Trigg2, Geoffrey Holmes1, and Ian H. Witten11Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand2Reel Two, P O Box 1538, Hamilton, New ZealandABSTRACTSummary: The Weka machine learning workbench providesa general-purpose environment for automatic classification,regression, clustering, and feature selection—common datamining problems in bioinformatics research. It contains anextensive collection of machine learning algorithms and datapre-processing methods complemented by graphical user in-terfaces for data exploration and the experimental compar-ison of different machine learning techniques on the sameproblem. Weka can process data given in the form of a sin-gle relational table. Its main objectives are to (a) assist usersin extracting useful information from data and (b) enable themto easily identify a suitable algorithm for generating an accu-rate predictive model from it.Availability: http://www.cs.waikato.ac.nz/ml/wekaContact: eibe@cs.waikato.ac.nzIntroductionBioinformatics research entails many problems that can becast as machine learning tasks. In classification or regressionthe task is to predict the outcome associated with a particularindividual given a feature vector describing that individual;in clustering, individuals are grouped together because theyshare certain properties; and in feature selection the task is toselect those features that are important in predicting the out-come for an individual.The Weka data mining suite provides algorithms for allthree of these problem types. In the bioinformatics arena ithas been used for automated protein annotation (Kretschmannet al., 2001; Bazzan et al., 2002), probe selection for gene-expression arrays (Tobler et al., 2002), experiments with auto-matic cancer diagnosis (Li et al., 2003a), developing a compu-tational model for frame-shifting sites (Bekaert et al., 2003),plant genotype discrimination (Taylor et al., 2002), classify-ing gene expression profiles (Li and Wong, 2002), and extract-ing rules from them (Li et al., 2003b). Many of the algorithmsin Weka are described in (Witten and Frank, 2000).Real datasets vary: no single algorithm is superior on alldata mining problems. The algorithm needs to match thestructure of the problem to obtain useful information or anaccurate model. The aim in developing Weka was to permit amaximum of flexibility when trying machine learning meth-∗To whom correspondence should be addressed.ods on new datasets. This includes algorithms for learningdifferent types of models (e.g. decision trees, rule sets, lineardiscriminants), feature selection schemes (fast filtering as wellas wrapper approaches) and pre-processing methods (e.g. dis-cretization, arbitrary mathematical transformations and com-binations of attributes). By providing a diverse set of methodsthat are available through a common interface, Weka makesit easy to compare different solution strategies based on thesame evaluation method and identify the one that is most ap-propriate for the problem at hand. It is implemented in Javaand runs on almost any computing platform.The Weka ExplorerThe main interface in Weka is the Explorer, shown in Figure 1.It has a set of panels, each of which can be used to performa certain task. The Preprocess panel, selected in Figure 1, re-trieves data from a file, SQL database or URL. (A limitation isthat all the data is kept in main memory, so subsampling maybe needed for very large datasets.) Then the data can be pre-processed using one of Weka’s filtering tools. For example,one can delete all instances (i.e. rows) in the data for which acertain attribute (i.e. column) has a particular value. An undofacility is provided to revert to an earlier state of the data ifneeded. The Preprocess panel also shows a histogram of theattribute that is currently selected and some statistics aboutit—histograms for all attributes can be shown simultaneouslyin a separate window.Once a dataset has been loaded (and perhaps processed byone or more filters), one of the other panels in the Explorer canbe used to perform further analysis. If the data entails a classi-fication or regression problem, it can be processed in the Clas-sify panel. This provides an interface to learning algorithmsfor classification and regression models (both are called “clas-sifiers” in Weka), and evaluation tools for analyzing the out-come of the learning process. Weka has implementations ofall major learning techniques for classification and regression:decision trees, rule sets, Bayesian classifiers, support vectormachines, logistic and linear regression, multi-layer percep-trons, and nearest-neighbor methods. It also contains “meta-learners” like bagging, boosting, stacking, and schemes thatperform automatic parameter tuning using cross-validation,cost-sensitive classification, etc. Learning algorithms can beevaluated using cross-validation or a hold-out set, and Wekaprovides standard numeric performance measures (e.g. accu-1Data Mining in Bioinformatics using WekaFigure 1: The Weka Explorer.racy, root mean squared error), as well as graphical meansfor visualizing classifier performance (e.g. ROC curves andprecision-recall curves). It is possible to visualize the pre-dictions of a classification or regression model, enabling theidentification of outliers, and to load and save models thathave been generated.The third panel in the Explorer, Cluster, gives access toWeka’s clustering algorithms. These include k-means, mix-tures of normal distributions with diagonal co-variance ma-trices estimated using EM, and a heuristic incremental hier-archical clustering scheme. Cluster assignments can be visu-alized and compared to actual clusters defined by one of theattributes in the data.Weka also contains algorithms for generating associationrules that can be used to identify relationships between groupsof attributes in the data. These are available from the Ex-plorer’s Associate panel. However, more interesting in thecontext of bioinformatics is the fifth panel, which offers meth-ods for identifying those subsets of attributes that are predic-tive of another (target) attribute in the data. Weka containsseveral methods for searching through the space of attributesubsets, as well as evaluation measures for attributes and at-tribute subsets. Search methods include best-first search, for-ward selection, genetic algorithms, and a simple ranking of at-tributes. Evaluation measures include correlation- and entropy-based criteria as well as the performance of a selected learningscheme (e.g. a decision tree learner) for a particular subset ofattributes. Different search and evaluation methods can becombined, making the system very flexible.The last panel in the Explorer, Visualization, shows a ma-trix of scatter plots for all pairs of attributes in the data. Anymatrix element can be selected and enlarged in a separate win-dow, where one can zoom in on subsets of the data and retrieveinformation about individual data points. A “jitter” option forexposing obscured data points is also provided.Other interfaces to WekaAll the learning techniques in Weka can be accessed from thecommand line, as part of shell scripts, or from within otherJava programs using the Weka API. Weka also contains an al-ternative graphical user interface, called “Knowledge Flow,”that can be used instead of the Explorer. It caters for a moreprocess-oriented view of data mining, where individual learn-ing components (represented by Java beans) can be connectedgraphically to create a “flow” of information. Finally, there isa third graphical user interface—the “Experimenter”—whichis designed for experiments that compare the performance of(multiple) learning schemes on (multiple) datasets. Experi-ments can be distributed across multiple computers runningremote experiment servers.AcknowledgementsMany people have contributed to the Weka project, in particu-lar Richard Kirkby, Ashraf Kibriya, and Bernhard Pfahringer,and we thank them all for their invaluable efforts. We wouldalso like to thank Yu Wang for suggesting that we write thisnote, and the New Zealand Foundation for Research, Science& Technology for funding the project.REFERENCESBazzan, A. L., et al. (2002) Automated annotation of key-words for proteins related to mycoplasmataceae using ma-chine learning techniques. Bioinformatics, 35S–43S.Bekaert, M., et al. (2003) Towards a computational model for-1 eukaryotic frameshifting sites. Bioinformatics, 327–335.Kretschmann, E., Fleischmann, W., and Apweiler, R. (2001)Automatic rule generation for protein annotation with theC4.5 data mining algorithm applied on SWISS-PROT.Bioinformatics, 920–926.Li, J., et al. (2003a) Discovery of significant rules for classi-fying cancer diagnosis data. Bioinformatics, 93ii–102ii.Li, J. and Wong, L. (2002) Identifying good diagnostic genegroups from gene expression profiles using the concept ofemerging patterns. Bioinformatics, 725–734.Li, J., et al. (2003b) Simple rules underlying gene expressionprofiles of more than six subtypes of acute lymphoblasticleukemia (ALL) patients. Bioinformatics, 71–78.Taylor, J., et al. (2002) Application of metabolomics toplant genotype discrimination using statistics and machinelearning. Bioinformatics, 241S–248S.Tobler, J. B., et al. (2002) Evaluating machine learning ap-proaches for aiding probe selection for gene-expressionarrays. Bioinformatics, 164S–171S.Witten, I. H. and Frank, E. (2000) Data Mining: PracticalMachine Learning Tools and Techniques with Java Imple-mentations. Morgan Kaufmann.2

(2003a) Discovery of signiﬁcant rules for classifying cancer diagnosis data.

(2003b) Simple rules underlying gene expression proﬁles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients.

Application of metabolomics to plant genotypediscriminationusing statistics andmachine learning.

Automated annotation of keywords for proteins related to mycoplasmataceaeusing machine learning techniques.

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations.

Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. Bioinformatics,

Identifying good diagnostic gene groups from gene expression proﬁles using the concept of emerging patterns.

Towards a computational model for -1 eukaryotic frameshifting sites.

Summary: The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection—common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental compar-ison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it

Data mining in bioinformatics using Weka

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