A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods

Authors:
Bernard Zenko;Ljupco Todorovski;Saso Dzeroski
Affiliations:
-;-;-
Venue:
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Year:
2001

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Abstract

Abstract. Meta decision trees (MTs) are a method for combining multiple classifiers. We present an integration of the algorithm MLC4.5 for learning MTs 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).