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AFC: ART-Based Fuzzy Classifier

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

Abstract

A novel ART-based neural classifier closely related to Fuzzy ARTMAP (FAM) and the classifying Fuzzy Min-Max Network (FMMN) of Simpson is presented. It emerged from the need to overcome the category proliferation problem. This problem consists in creating an excessive number of prototypes needed to represent pattern clusters in the input space and their relations to the output space. Due to overtraining, FAM and FMMN often form redundant prototypes which leads to increase in resource requirements. In contrast to some recent solutions of the category proliferation problem, the proposed ART-based Fuzzy Classifier (AFC) can alleviate it while preserving a very important ART property, namely the on-line learning ability. AFC differs from FAM and FMMN in several key aspects. First, it uses a non-flat asymmetric choice function calculated on the basis of the positions of cluster centroids. It also utilizes different concepts of hyperbox size control and overlap resolving.

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Authors and Affiliations

  1. University of Tuebingen, Tuebingen, D-72076, Germany

    Elena P. Sapozhnikova & Wolfgang Rosenstiel

Authors
  1. Elena P. Sapozhnikova
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  2. Wolfgang Rosenstiel
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Sapozhnikova, E.P., Rosenstiel, W. (2003). AFC: ART-Based Fuzzy Classifier. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_5

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  • DOI: https://doi.org/10.1007/978-3-540-45226-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

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