A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods








Bernard Zenko, Ljupco Todorovski and Saso Dzeroski












Abstract




Meta decision trees (MDTs) are a method for combining multiple classifiers. We present an integration of the algorithm MLC4.5 for learning MDTs into the Weka data mining suite. We compare classifier ensembles combined with MDTs to bagged and boosted decision trees, and to classifier ensembles combined with other methods: voting and stacking with three different meta-level classifiers (ordinary decision trees, naive Bayes, and multi-response linear regression - MLR).







Poster paper [ps] [pdf]
























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Bernard Zenko
Created: November 2001