A HYBRID SVM AND SUPERVISED LEARNING APPROACH TO FUZZY MIN-MAX HYPERBOX CLASSIFIERS

摘要

In this paper, a fuzzy min-max hyperbox classifier is designed to solve M-class classification problems using a hybrid SVM and supervised learning approach. In order to solve a classification problem, a set of training patterns is gathered from a considered classification problem. However, the training set may include several noisy patterns. In order to delete the noisy patterns from the training set, the support vector machine is applied to find the noisy patterns so that the remaining training patterns can describe the behavior of the considered classification system well. Subsequently, a supervised learning method is proposed to generate fuzzy min-max hyperboxes for the remaining training patterns so that the generated fuzzy min-max hyperbox classifier has good generalization performance. Finally, the Iris data set is considered to demonstrate the good performance of the proposed approach for solving this classification problem.