A monotonic measure for optimal feature selection

Huan Liu, Hiroshi Motoda, Manoranjan Dash

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

40 Scopus citations

Abstract

Feature selection is a problem of choosing a subset of relevant features. In general,only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distance-based measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed tobe complete but not exhaustive. Experiments are conducted for verification.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML-1998 - 10th European Conference on Machine Learning, Proceedings
EditorsClaire Nédellec, Céline Rouveirol
PublisherSpringer Verlag
Pages101-106
Number of pages6
ISBN (Print)3540644172, 9783540644170
StatePublished - 1998
Event10th European Conference on Machine Learning, ECML 1998 - Chemnitz, Germany
Duration: Apr 21 1998Apr 23 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1398
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th European Conference on Machine Learning, ECML 1998
CountryGermany
CityChemnitz
Period4/21/984/23/98

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, H., Motoda, H., & Dash, M. (1998). A monotonic measure for optimal feature selection. In C. Nédellec, & C. Rouveirol (Eds.), Machine Learning: ECML-1998 - 10th European Conference on Machine Learning, Proceedings (pp. 101-106). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1398). Springer Verlag.