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Study on interpretable fuzzy classification system based on neural networks

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4 Author(s)
Qin Yong ; Sch. of Traffic & Transp., Beijing Jiaotong Univ., Beijing, China ; Xing Zong-yi ; Jia Li-min ; Wu Ying-ying

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.

Published in:

ICCAS-SICE, 2009

Date of Conference:

18-21 Aug. 2009

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