Title :
Study on interpretable fuzzy classification system based on neural networks
Author :
Yong, Qin ; Zong-Yi, Xing ; Li-Min, Jia ; Ying-Ying, Wu
Author_Institution :
Sch. of Traffic & Transp., Beijing Jiaotong Univ., Beijing, China
Abstract :
This paper describes a comprehensive method to construct fuzzy classification system considering both precision and interpretability. Fuzzy classification system, initialized by modified Gath-Geva fuzzy clustering algorithm, is transformed into neural network. After training the neural network, fuzzy sets similarity measure is adopt to merge redundant fuzzy sets to improve interpretability, and a constraint genetic algorithm is applied to improve precision. The simulation result on Iris data problem demonstrates the effectiveness of the proposed method.
Keywords :
fuzzy set theory; genetic algorithms; neural nets; Iris data problem; constraint genetic algorithm; fuzzy set theory; interpretable fuzzy classification system; modified Gath-Geva fuzzy clustering algorithm; neural networks; Clustering algorithms; Electronic mail; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Iris; Neural networks; Telecommunication traffic; Transportation; Fuzzy classification system; interpretability; neural network;
Conference_Titel :
ICCAS-SICE, 2009
Conference_Location :
Fukuoka
Print_ISBN :
978-4-907764-34-0
Electronic_ISBN :
978-4-907764-33-3