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Combining Classifiers with Meta Decision Trees
Authors:Todorovski  Ljupčo  Džeroski  Sašo
Affiliation:(1) Department of Intelligent Systems, Jozcaronef Stefan Institute, Jamova 39, Ljubljana, Slovenia
Abstract:The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier should be used to obtain a prediction. We present an algorithm for learning MDTs based on the C4.5 algorithm for learning ordinary decision trees (ODTs). An extensive experimental evaluation of the new algorithm is performed on twenty-one data sets, combining classifiers generated by five learning algorithms: two algorithms for learning decision trees, a rule learning algorithm, a nearest neighbor algorithm and a naive Bayes algorithm. In terms of performance, stacking with MDTs combines classifiers better than voting and stacking with ODTs. In addition, the MDTs are much more concise than the ODTs and are thus a step towards comprehensible combination of multiple classifiers. MDTs also perform better than several other approaches to stacking.
Keywords:ensembles of classifiers  meta-level learning  combining classifiers  stacking  decision trees
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