Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning

A new method to construct multiple classifier system by making diverse base classifiers using weight tuning using an evolutionary method to optimize efficiency of each base classifier to distinguish one class of input data in this step.

Abstract

This article presents a new method to construct multiple classifier system by making diverse base classifiers using weight tuning. In the method presented, base classifiers are multilayer perceptions which creates diverse base classifiers using a three-step procedure. In the first step, base classifiers are trained for acceptable accuracy. In the second step, a weight tuning process tunes their weights such that each one can distinguish one class of input data from the others with highest possible accuracy. An evolutionary method is used to optimize efficiency of each base classifier to distinguish one class of input data in this step. In the third step, a new method combines the results of the base classifiers. As diversity is measured and monitored throughout the entire procedure, it is measured using a confusion matrix. Superiority of the proposed method is discussed using several known classifier fusion methods and known benchmark datasets.