Adaptive fuzzy rule-based classification system integrating both expert knowledge and data
Author
Tang, Wenyin
Mao, Kezhi
Mak, Lee Onn
Ng, Gee Wah
Date of Issue
2012Conference Name
IEEE International Conference on Tools with Artificial Intelligence (24th : 2012 : Athens, Greece)
School
School of Electrical and Electronic Engineering
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.
Subject
DRNTU::Engineering::Electrical and electronic engineering
Type
Conference Paper
Collections
http://dx.doi.org/10.1109/ICTAI.2012.114
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