Adaptive Fuzzy Rule-Based Classification System Integrating Both Expert Knowledge and Data

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Wenyin Tang, Ke Zhi Mao, Lee Onn Mak, Gee Wah Ng

Published: 2012, Last Modified: 05 Nov 2025ICTAI 2012EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper presents an adaptive fuzzy rule-based classification system using a new hybrid modeling method that integrates both expert knowledge and new knowledge learnt from data. Inspired by human learning, the membership functions of fuzzy rules are optimized based on a hybrid error function that combines errors caused by the class predefined by expert knowledge and nearby historical data. The weights of the two errors can be adjusted by a conservative parameter. Experimental results show that our method significantly reduces classification ambiguity in 9 datasets.