The bagging and n2-classifiers based on rules induced by MODLEM

Abstract

An application of the rule induction algorithm MODLEM to construct multiple classifiers is studied. Two different such classifiers are considered: the bagging approach, where classifiers are generated from different samples of the learning set, and the n2classifier, which is specialized for solving multiple class learning problems. This paper reports results of an experimental comparison of these multiple classifiers and the single, MODLEM based, classifier performed on several data sets.