Constructive induction: A preprocessor

Yuh-Jyh Hu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996, Proceedings
EditorsGordon McCalla
PublisherSpringer Verlag
Pages249-256
Number of pages8
ISBN (Print)3540612912, 9783540612919
DOIs
StatePublished - 1 Jan 1996
Event11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996 - Toronto, Canada
Duration: 21 May 199624 May 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1081
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996
CountryCanada
CityToronto
Period21/05/9624/05/96

Keywords

  • Classification
  • Constructive induction
  • Learning

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    Hu, Y-J. (1996). Constructive induction: A preprocessor. In G. McCalla (Ed.), Advances in Artificial Intelligence - 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996, Proceedings (pp. 249-256). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1081). Springer Verlag. https://doi.org/10.1007/3-540-61291-2_56