Combining Multiple Models with Meta Decision Trees








Ljupco Todorovski and Saso Dzeroski












Abstract




The paper introduces meta decision trees (MDTs), a novel method for combining multiple models. Instead of giving a prediction, MDT leaves specify which model 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 models 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, MDTs combine models better than voting and stacking with ODTs. In addition, MDTs are much more concise than ODTs used for stacking and are thus a step towards comprehensible combination of multiple models.







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Bernard Zenko
Created: November 2001