Combining Methods in Supervised Classification: a Comparative Study on Discrete and Continuous Problems

Abstract

Often in discriminant analysis several models are estimated but based on some validation criterion, a single model is selected. In the purpose of taking profit from several potential models, classification rules combining models are considered in this article. More precisely two ways of combining models are considered: a serial combining method and a hierarchical combining method. Serial combining is a convex linear combination of a finite number of models. Hierarchical combining method leads to nested models structured in a binary tree. In this paper, several combining methods resorting from both points of view are presented and their performances are assessed on discrete and continuous classification problems.