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Trading off between Misclassification, Recognition and Generalization in Data Mining with Continuous Features

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Developments in Applied Artificial Intelligence (IEA/AIE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2358))

Abstract

This paper aims at developing a data mining approach for classification rule representation and automated acquisition from numerical data with continuous attributes. The classification rules are crisp and described by ellipsoidal regions with different attributes for each individual rule. A regularization model trading off misclassification rate, recognition rate and generalization ability is presented and applied to rule refinement. A regularizing data mining algorithm is given, which includes self-organizing map network based clustering techniques, feature selection using breakpoint technique, rule initialization and optimization, classifier structure and usage. An Illustrative example demonstrates the applicability and potential of the proposed techniques for domains with continuous attributes.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3083, Australia

    Dianhui Wang & Tharam Dillon

  2. Department of Computer Science and Software Engineering, Newcastle University, Newcastle, Australia

    Elizabeth Chang

Authors
  1. Dianhui Wang
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  2. Tharam Dillon
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  3. Elizabeth Chang
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Editor information

Editors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, John Street, Hawthorn, Victoria, Australia, 3122

    Tim Hendtlass

  2. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

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© 2002 Springer-Verlag Berlin Heidelberg

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Wang, D., Dillon, T., Chang, E. (2002). Trading off between Misclassification, Recognition and Generalization in Data Mining with Continuous Features. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_30

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  • DOI: https://doi.org/10.1007/3-540-48035-8_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43781-9

  • Online ISBN: 978-3-540-48035-8

  • eBook Packages: Springer Book Archive

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