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Efficient and Comprehensible Local Regression

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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

This paper describes an approach to multivariate regression that aims at improving the computational efficiency and comprehensibility of local regression techniques. Local regression modeling is known for its ability to accurately approximate quite diverse regression surfaces with high accuracy. However, these methods are also known for being computationally demanding and for not providing any comprehensible model of the data. These two characteristics can be regarded as major drawbacks in the context of a typical data mining scenario. The method we describe tackles these problems by integrating local regression within a partition-based induction method.

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References

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Author information

Authors and Affiliations

  1. LIACC-FEP, University of Porto, R. Campo Alegre, 823 - 4150, Porto, Portugal

    Luís Torgo

Authors
  1. Luís Torgo
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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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Torgo, L. (2000). Efficient and Comprehensible Local Regression. 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_44

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  • DOI: https://doi.org/10.1007/3-540-45571-X_44

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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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