The MSFAM: a modified fuzzy ARTMAP system

  • Authors:

  • Mu-Chun Su;Wei-Zhe Lu;Jonathan Lee;Gwo-Dong Chen;Chen-Chiung Hsieh

  • Affiliations:

  • National Central University, Department of Computer Science and Information Engineering, Taiwan, Republic of China;National Central University, Department of Computer Science and Information Engineering, Taiwan, Republic of China;National Central University, Department of Computer Science and Information Engineering, Taiwan, Republic of China;National Central University, Department of Computer Science and Information Engineering, Taiwan, Republic of China;Institute for Information Industry, Integration Technology Laboratory, Taiwan, Republic of China

  • Venue:
  • Pattern Analysis & Applications
  • Year:
  • 2005

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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.