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Prototype Generation Based on Instance Filtering and Averaging

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

We propose a new algorithm, called Prototype Generation and Filtering (PGF), which combines the strength of instance-filtering and instance-averaging techniques. PGF is able to generate representative prototypes while eliminating noise and exceptions. We also introduce a distance measure incorporating the class label entropy information for the prototypes. Experiments have been conducted to compare our PGF algorithm with pure instance filtering, pure instance averaging, as well as state-of-the-art algorithms such as C4.5 and KNN. The results demonstrate that PGF can significantly reduce the size of the data while maintaining and even improving the classification performance.

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Author information

Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Chi-Kin Keung & Wai Lam

Authors
  1. Chi-Kin Keung
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  2. Wai Lam
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Keung, CK., Lam, W. (2000). Prototype Generation Based on Instance Filtering and Averaging. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_17

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  • DOI: https://doi.org/10.1007/3-540-45571-X_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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