A Study on Threshold Optimization Methods in Fuzzy Classifiers with a Threshold-based Reject Option

Abstract

In cases where misclassification can have serious consequences, such as healthcare and finance, classification systems must ensure high interpretability and reliability. Fuzzy classifiers can provide lin-(breakpoint)guistic explanations for their classification results, thus offering high interpretability. To improve their reliability, we have proposed fuzzy classifiers with a threshold-based reject option, which rejects classifica-(breakpoint)tions with low confidence. A traditional method optimizes the thresholds using constrained single-objective optimization. However, there is a possibility of getting trapped in local optima. In this study, we formu-(breakpoint)late the threshold optimization as a bi-objective optimization problem to minimize misclassification and rejection rates. We apply an evolutionary multi-objective optimization algorithm to this problem in order to find the optimal thresholds to improve the reliability and classification performance of fuzzy classifiers.