Reliability Learning for Interval Type-2 TSK Fuzzy Logic System with its Application to Medical Diagnosis

Abstract

To apply intelligent model in serious practical applications like medical diagnosis, the reliability and interpretability of the model are very important to users. Among the existing intelligent models, type-2 fuzzy systems are distinctive in interpretability and modeling uncertainty. However, like most existing models, the reliability determination of fuzzy system for recognition task training is an unsolved problem. In this study, a method of constructing minimax probability interval type-2 TSK fuzzy logic system classifier (MP-IT2TSK-FLSC) based on reliability learning is proposed. The classifier can provide the lower limit of the correct classification of the model and is an important index to quantify the reliability of the model. Experimental results on medical datasets have demonstrated the advantages of this method, exhibiting remarkable interpretability and reliability of the proposed fuzzy classifier.