A Proposal of Ensemble Learning “pdi-Boosting” Inheriting Virtual Data and Fuzzy Rules

Abstract

Recently, we have been increasing interest in ensemble learning. In particular, Bagging and Boosting are useful methods. Bagging is a method of learning more than one classifier independently for training data, and Boosting is a method of learning more than one classifier dependent on each other. In this paper, we propose a new ensemble learning ”pdi-Boosting,” which inherits virtual data and fuzzy rules between classifiers of pdi-Bagging. In pdi-Boosting, the discrimination rate is improved, and the robustness against noise data is also improved because of increasing the amount of data due to the inheritance of virtual data. We define here the inheritance method for virtual data and fuzzy rules, and formulate pdi-Boosting. We will also discuss the usefulness of pdi-Boosting.