DESIGN OF A FUZZY MIN-MAX HYPERBOX CLASSIFIER USING A SUPERVISED LEARNING METHOD

ABSTRACT

This investigation proposes a fuzzy min-max hyperbox classifier to solve M-class classification problems. In the proposed fuzzy min-max hyperbox classifier, a supervised learning method is implemented to generate min-max hyperboxes for the training patterns in each class so that the generated fuzzy min-max hyperbox classifier has a perfect classification rate in the training set. However, the 100% correct classification of the training set generally leads to overfitting. In order to improve this drawback, a procedure is employed to decrease the complexity of the input decision boundaries so that the generated fuzzy hyperbox classifier has a good generalization performance. Finally, two benchmark data sets are considered to demonstrate the good performance of the proposed approach for solving this classification problem.