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Minimum Message Length Criterion for Second-Order Polynomial Model Discovery

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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 proposes a method based on the Minimum Message Length (MML) Principle for the task of discovering polynomial models up to the second order. The method is compared with a number of other selection criteria in the ability to, in an automated manner, discover a model given the generated data. Of particular interest is the ability of the methods to discover (1) second-order independent variables, (2) independent variables with weak causal relationships with the target variable given a small sample size, and (3) independent variables with weak links to the target variable but strong links from other variables which are not directly linked with the target variable. A common non-backtracking search strategy has been developed and is used with all of the model selection criteria.

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Author information

Authors and Affiliations

  1. School of Computer Science and Software Engineering, Monash University, Clayton, Vic, 3168, Australia

    Grace W Rumantir

Authors
  1. Grace W Rumantir
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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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Rumantir, G.W. (2000). Minimum Message Length Criterion for Second-Order Polynomial Model Discovery. 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_7

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  • DOI: https://doi.org/10.1007/3-540-45571-X_7

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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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