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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc (build 1.5.0_10) on Fri Jan 26 16:36:09 NZDT 2007 --><TITLE>weka.classifiers.meta</TITLE><META NAME="keywords" CONTENT="weka.classifiers.meta package"><LINK REL ="stylesheet" TYPE="text/css" HREF="../../../stylesheet.css" TITLE="Style"><SCRIPT type="text/javascript">function windowTitle(){ parent.document.title="weka.classifiers.meta";}</SCRIPT><NOSCRIPT></NOSCRIPT></HEAD><BODY BGCOLOR="white" onload="windowTitle();"><!-- ========= START OF TOP NAVBAR ======= --><A NAME="navbar_top"><!-- --></A><A HREF="#skip-navbar_top" title="Skip navigation links"></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0" SUMMARY=""><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3" SUMMARY=""> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../overview-summary.html"><FONT CLASS="NavBarFont1"><B>Overview</B></FONT></A> </TD> <TD BGCOLOR="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Package</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <FONT CLASS="NavBarFont1">Class</FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-tree.html"><FONT CLASS="NavBarFont1"><B>Tree</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../deprecated-list.html"><FONT CLASS="NavBarFont1"><B>Deprecated</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../index-all.html"><FONT CLASS="NavBarFont1"><B>Index</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../help-doc.html"><FONT CLASS="NavBarFont1"><B>Help</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="http://www.cs.waikato.ac.nz/ml/weka/" target="_blank"><FONT CLASS="NavBarFont1"><B>Weka's home</B></FONT></A> </TD> </TR></TABLE></TD><TD ALIGN="right" VALIGN="top" ROWSPAN=3><EM></EM></TD></TR><TR><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../../weka/classifiers/lazy/kstar/package-summary.html"><B>PREV PACKAGE</B></A> <A HREF="../../../weka/classifiers/meta/ensembleSelection/package-summary.html"><B>NEXT PACKAGE</B></A></FONT></TD><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../../index.html?weka/classifiers/meta/package-summary.html" target="_top"><B>FRAMES</B></A> <A HREF="package-summary.html" target="_top"><B>NO FRAMES</B></A> <SCRIPT type="text/javascript"> <!-- if(window==top) { document.writeln('<A HREF="../../../allclasses-noframe.html"><B>All Classes</B></A>'); } //--></SCRIPT><NOSCRIPT> <A HREF="../../../allclasses-noframe.html"><B>All Classes</B></A></NOSCRIPT></FONT></TD></TR></TABLE><A NAME="skip-navbar_top"></A><!-- ========= END OF TOP NAVBAR ========= --><HR><H2>Package weka.classifiers.meta</H2><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2"><B>Class Summary</B></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/AdaBoostM1.html" title="class in weka.classifiers.meta">AdaBoostM1</A></B></TD><TD>Class for boosting a nominal class classifier using the Adaboost M1 method.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/AdditiveRegression.html" title="class in weka.classifiers.meta">AdditiveRegression</A></B></TD><TD>Meta classifier that enhances the performance of a regression base classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/AttributeSelectedClassifier.html" title="class in weka.classifiers.meta">AttributeSelectedClassifier</A></B></TD><TD>Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/Bagging.html" title="class in weka.classifiers.meta">Bagging</A></B></TD><TD>Class for bagging a classifier to reduce variance.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/ClassificationViaRegression.html" title="class in weka.classifiers.meta">ClassificationViaRegression</A></B></TD><TD>Class for doing classification using regression methods.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/CostSensitiveClassifier.html" title="class in weka.classifiers.meta">CostSensitiveClassifier</A></B></TD><TD>A metaclassifier that makes its base classifier cost-sensitive.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/CVParameterSelection.html" title="class in weka.classifiers.meta">CVParameterSelection</A></B></TD><TD>Class for performing parameter selection by cross-validation for any classifier.<br/> <br/> For more information, see:<br/> <br/> R.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/Dagging.html" title="class in weka.classifiers.meta">Dagging</A></B></TD><TD>This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/Decorate.html" title="class in weka.classifiers.meta">Decorate</A></B></TD><TD>DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/END.html" title="class in weka.classifiers.meta">END</A></B></TD><TD>A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.<br/> <br/> For more info, check<br/> <br/> Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/EnsembleSelection.html" title="class in weka.classifiers.meta">EnsembleSelection</A></B></TD><TD>Combines several classifiers using the ensemble selection method.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/FilteredClassifier.html" title="class in weka.classifiers.meta">FilteredClassifier</A></B></TD><TD>Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/Grading.html" title="class in weka.classifiers.meta">Grading</A></B></TD><TD>Implements Grading.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/GridSearch.html" title="class in weka.classifiers.meta">GridSearch</A></B></TD><TD>Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.<br/> <br/> The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/LogitBoost.html" title="class in weka.classifiers.meta">LogitBoost</A></B></TD><TD>Class for performing additive logistic regression.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/MetaCost.html" title="class in weka.classifiers.meta">MetaCost</A></B></TD><TD>This metaclassifier makes its base classifier cost-sensitive using the method specified in<br/> <br/> Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/MultiBoostAB.html" title="class in weka.classifiers.meta">MultiBoostAB</A></B></TD><TD>Class for boosting a classifier using the MultiBoosting method.<br/> <br/> MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/MultiClassClassifier.html" title="class in weka.classifiers.meta">MultiClassClassifier</A></B></TD><TD>A metaclassifier for handling multi-class datasets with 2-class classifiers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/MultiScheme.html" title="class in weka.classifiers.meta">MultiScheme</A></B></TD><TD>Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/OrdinalClassClassifier.html" title="class in weka.classifiers.meta">OrdinalClassClassifier</A></B></TD><TD>Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.<br/> <br/> For more information see: <br/> <br/> Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/RacedIncrementalLogitBoost.html" title="class in weka.classifiers.meta">RacedIncrementalLogitBoost</A></B></TD><TD>Classifier for incremental learning of large datasets by way of racing logit-boosted committees.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/RandomCommittee.html" title="class in weka.classifiers.meta">RandomCommittee</A></B></TD><TD>Class for building an ensemble of randomizable base classifiers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/RandomSubSpace.html" title="class in weka.classifiers.meta">RandomSubSpace</A></B></TD><TD>This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/RegressionByDiscretization.html" title="class in weka.classifiers.meta">RegressionByDiscretization</A></B></TD><TD>A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/Stacking.html" title="class in weka.classifiers.meta">Stacking</A></B></TD><TD>Combines several classifiers using the stacking method.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/StackingC.html" title="class in weka.classifiers.meta">StackingC</A></B></TD><TD>Implements StackingC (more efficient version of stacking).<br/> <br/> For more information, see<br/> <br/> A.K.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/ThresholdSelector.html" title="class in weka.classifiers.meta">ThresholdSelector</A></B></TD><TD>A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/meta/Vote.html" title="class in weka.classifiers.meta">Vote</A></B></TD><TD>Class for combining classifiers.</TD></TR></TABLE> <P><DL></DL><HR><!-- ======= START OF BOTTOM NAVBAR ====== --><A NAME="navbar_bottom"><!-- --></A><A HREF="#skip-navbar_bottom" title="Skip navigation links"></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0" SUMMARY=""><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_bottom_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3" SUMMARY=""> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../overview-summary.html"><FONT CLASS="NavBarFont1"><B>Overview</B></FONT></A> </TD> <TD BGCOLOR="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Package</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <FONT CLASS="NavBarFont1">Class</FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-tree.html"><FONT CLASS="NavBarFont1"><B>Tree</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../deprecated-list.html"><FONT CLASS="NavBarFont1"><B>Deprecated</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../index-all.html"><FONT CLASS="NavBarFont1"><B>Index</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../../help-doc.html"><FONT CLASS="NavBarFont1"><B>Help</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="http://www.cs.waikato.ac.nz/ml/weka/" target="_blank"><FONT CLASS="NavBarFont1"><B>Weka's home</B></FONT></A> </TD> </TR></TABLE></TD><TD ALIGN="right" VALIGN="top" ROWSPAN=3><EM></EM></TD></TR><TR><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT 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