A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods
Bernard Ženko, Ljupčo Todorovski, Sašo Džeroski · 2002
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). Meta decision trees. Techniques for combining predictions obtained from multiple base-level classifiers can be clustered in three combining frameworks: voting (used in bagging and boosting), stacked generalization or stacking [7] and cascading. Meta decision trees (MDTs) [5] adopt the stacking framework of combining base-level classifiers. The difference between meta and ordinary decision trees (ODTs) is that MDT leaves specify which base-level classifier should be used, instead of predicting the class value directly. Th...