Tree pruning for output coded ensembles

Abstract

Output coding is a method of converting a multiclass problem into several binary subproblems and gives an ensemble of binary classifiers. Like other ensemble methods, its performance depends on the accuracy and diversity of base classifiers. If a decision tree is chosen as base classifier the issue of tree pruning needs to be addressed. In this paper we investigate the effect of six methods of pruning on ensembles of trees generated by error-correcting output code (ECOC). Our results show that error-based pruning outperforms on most datasets but it is better not to prune than to select a single pruning strategy for all datasets.