TY - GEN
T1 - Reliability Learning for Interval Type-2 TSK Fuzzy Logic System with its Application to Medical Diagnosis
AU - Lou, Qiongdan
AU - Deng, Zhaohong
AU - Wang, Guanjin
AU - Choi, Kup Sze
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - classification
KW - minimax probability decision
KW - model reliability
KW - Type-2 fuzzy logic system
UR - http://www.scopus.com/inward/record.url?scp=85091524317&partnerID=8YFLogxK
U2 - 10.1109/ISKE47853.2019.9170445
DO - 10.1109/ISKE47853.2019.9170445
M3 - Conference article published in proceeding or book
AN - SCOPUS:85091524317
T3 - Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
SP - 43
EP - 50
BT - Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
A2 - Zou, Li
A2 - Fang, Lingling
A2 - Fu, Bo
A2 - Niu, Panpan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
Y2 - 14 November 2019 through 16 November 2019
ER -