Adaptive fuzzy rule-based classification system integrating both expert knowledge and data
Mak, Lee Onn
Ng, Gee Wah
Date of Issue2012
IEEE International Conference on Tools with Artificial Intelligence (24th : 2012 : Athens, Greece)
School of Electrical and Electronic Engineering
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.
DRNTU::Engineering::Electrical and electronic engineering