A Proposal of Directional Virtual Data in pdi-BoostingG

Abstract

We have proposed pdi-BoostingG that generates virtual data as a kind of ensemble learning. In this method, region G is set to generate additional fuzzy rules around misclassified data, and virtual data is generated within this region G. Generated virtual data and added fuzzy rules are inherited among layers of ensemble learning. In this paper, we propose a method to generate virtual data with a normal distribution around the internal division points of different classes of misclassified data, with directivity to the central position of the class. Since generated virtual data is inherited between layers together with fuzzy rules, deep inference of fuzzy rules is realized. We discuss here how to generate virtual data and discuss the features of the proposed method from the results of the discrimination rate using numerical examples.