Adaptive fuzzy rule-based classification system integrating both expert knowledge and data

• Main Authors: Ng, Gee Wah, Tang, Wenyin, Mao, Kezhi, Mak, Lee Onn
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/99301; http://hdl.handle.net/10220/12873
• Institution: Nanyang Technological University

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.