Reliability learning for interval Type-2 TSK fuzzy logic system with its application to medical diagnosis

Lou, Q., Deng, Z., Wang, G.ORCID: 0000-0002-5258-0532 and Choi, K-S (2019) Reliability learning for interval Type-2 TSK fuzzy logic system with its application to medical diagnosis. In: 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) 2019, 14 - 16 November 2019, Dalian, China

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.

Item Type:	Conference Paper
Murdoch Affiliation(s):	Information Technology, Mathematics and Statistics
URI:	http://researchrepository.murdoch.edu.au/id/eprint/58040

Item Control Page