Agent based multi-objective approach to generating interpretable fuzzy systems

Hanli WANG; Sam KWONG; Yaochu JIN; Chi-Ho TSANG

doi:10.1007/11399346_15

Agent based multi-objective approach to generating interpretable fuzzy systems

Hanli WANG, Sam KWONG, Yaochu JIN, Chi-Ho TSANG

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

1 Citation (Scopus)

Abstract

Interpretable fuzzy systems are very desirable for human users to study complex systems. To meet this end, an agent based multi-objective approach is proposed to generate interpretable fuzzy systems from experimental data. The proposed approach can not only generate interpretable fuzzy rule bases, but also optimize the number and distribution of fuzzy sets. The trade-off between accuracy and interpretability of fuzzy systems derived from our agent based approach is studied on some benchmark classification problems in the literature. © 2006 Springer-Verlag Berlin Heidelberg.

Original language	English
Title of host publication	Studies in Computational Intelligence
Pages	339-364
DOIs	https://doi.org/10.1007/11399346_15
Publication status	Published - 2006
Externally published	Yes

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10.1007/11399346_15

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abstract = "Interpretable fuzzy systems are very desirable for human users to study complex systems. To meet this end, an agent based multi-objective approach is proposed to generate interpretable fuzzy systems from experimental data. The proposed approach can not only generate interpretable fuzzy rule bases, but also optimize the number and distribution of fuzzy sets. The trade-off between accuracy and interpretability of fuzzy systems derived from our agent based approach is studied on some benchmark classification problems in the literature. {\textcopyright} 2006 Springer-Verlag Berlin Heidelberg.",

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AB - Interpretable fuzzy systems are very desirable for human users to study complex systems. To meet this end, an agent based multi-objective approach is proposed to generate interpretable fuzzy systems from experimental data. The proposed approach can not only generate interpretable fuzzy rule bases, but also optimize the number and distribution of fuzzy sets. The trade-off between accuracy and interpretability of fuzzy systems derived from our agent based approach is studied on some benchmark classification problems in the literature. © 2006 Springer-Verlag Berlin Heidelberg.

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BT - Studies in Computational Intelligence

ER -

Agent based multi-objective approach to generating interpretable fuzzy systems

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