Multi-class boosting with fuzzy decision trees
Marco Barsacchi, Alessio Bechini, Francesco Marcelloni · 2017
Boosting is a simple and effective procedure that combines several weak learners with the aim of generating a strong classifier. Multi-class boosting has been only recently studied in the context of crisp classifiers, showing encouraging performances. In this paper, we propose FDT-Boost, a boosting approach shaped according to the multi-class SAMME-AdaBoost scheme, that employs size-constrained fuzzy binary decision trees as weak classifiers. We test FDT-Boost on twenty-three classification benchmarks. By comparing our approach with FURIA, one of the most popular fuzzy classifiers, and with a fuzzy binary decision tree, we show that our approach is accurate, yet keeping low the model complexity in terms of total number of leaf nodes.