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An Efficient Data Compression Approach to the Classification Task

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

The paper illustrates a data compression approach to classification, based on a stochastic gradient algorithm for the minimization of the average misclassification risk performed by a Labeled Vector Quantizer. The main properties of the approach can be summarized in terms of both the efficiency of the learning process, and the efficiency and accuracy of the classification process. The approach is compared with the strictly related nearest neighbor rule, and with two data reduction algorithms, SVM and IB2, on a set of real data experiments taken from the UCI repository.

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Author information

Authors and Affiliations

  1. Computer Science Institute, University of Ancona, via Brecce Bianche, 60131, Ancona, Italy

    Claudia Diamantini & Maurizio Panti

Authors
  1. Claudia Diamantini
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  2. Maurizio Panti
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Diamantini, C., Panti, M. (2001). An Efficient Data Compression Approach to the Classification Task. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_47

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  • DOI: https://doi.org/10.1007/3-540-45357-1_47

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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