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The MSFAM: a modified fuzzy ARTMAP system

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Abstract

A fuzzy ARTMAP system is a system for incremental supervised learning of recognition categories and multi-dimensional maps in response to an arbitrary sequence of analog or binary input vectors. Fuzzy ARTMAP systems have been benchmarked against a variety of machine learning, neural networks, and genetic algorithms with considerable success. Owing to many appealing properties, fuzzy ARTMAP systems provide a natural basis for many researchers. Many different approaches have been proposed to modify fuzzy ARTMAP systems. In this paper, we propose a new approach to modifying a fuzzy ARTMAP system. We refer to the new system as the modified and simplified fuzzy ARTMAP (MSFAM) system. The aims of MSFAM systems are not only to reduce the architectural redundancy of the fuzzy ARTMAP system, but also to make extracted rules more comprehensible and concise. Four data sets were used for demonstrating the performance of the proposed MSFAM system.

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Acknowledgements

This work is supported by the MOE Program for Promoting Academic Excellence of Universities under grant number EX-91-E-FA06-4-4, the National Science Council, Taiwan, R.O.C, under grant number NSC 93-2524-S-008-002, and the Ministry of Economic Affairs under grant number 93-EC-17-A-02-S1-029.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Central University, Taiwan, Republic of China

    Mu-Chun Su, Wei-Zhe Lu, Jonathan Lee & Gwo-Dong Chen

  2. Integration Technology Laboratory, Institute for Information Industry, Taiwan, Republic of China

    Chen-Chiung Hsieh

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  1. Mu-Chun Su
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  2. Wei-Zhe Lu
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  3. Jonathan Lee
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  4. Gwo-Dong Chen
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  5. Chen-Chiung Hsieh
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Correspondence to Mu-Chun Su.

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Su, MC., Lu, WZ., Lee, J. et al. The MSFAM: a modified fuzzy ARTMAP system. Pattern Anal Applic 8, 1–16 (2005). https://doi.org/10.1007/s10044-004-0229-y

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