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Constructive induction: A preprocessor

  • Learning I: Induction
  • Conference paper
  • First Online:
Advances in Artifical Intelligence (Canadian AI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1081))

  • 139 Accesses

Abstract

Inductive algorithms rely strongly on their representational biases. Representational inadequacy can be mitigated by constructive induction. This paper introduces the notion of relative gain measure and describes a new constructive induction algorithm (GALA) which generates a small number of new attributes from existing nominal or real-valued attributes. Unlike most previous research on constructive induction, our techniques are designed for use in preprocessing data set for subsequent use by any standard selective learning algorithms. We present results which demonstrate the effectiveness of GALA on both artificial and real domains with respect to C4.5 and CN2.

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References

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Author information

Authors and Affiliations

  1. Information and Computer Science Department, University of California, Irvine

    Yuh-Jyh Hu

Authors
  1. Yuh-Jyh Hu
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Editor information

Gordon McCalla

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© 1996 Springer-Verlag Berlin Heidelberg

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Hu, YJ. (1996). Constructive induction: A preprocessor. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_56

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  • DOI: https://doi.org/10.1007/3-540-61291-2_56

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61291-9

  • Online ISBN: 978-3-540-68450-3

  • eBook Packages: Springer Book Archive

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