A heuristic method for selecting support features from large datasets

Hong Seo Ryoo, In Yong Jang

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

Abstract

For feature selection in machine learning, set covering (SC) is most suited, for it selects support features for data under analysis based on the individual and the collective roles of the candidate features. However, the SC-based feature selection requires the complete pair-wise comparisons of the members of the different classes in a dataset, and this renders the meritorious SC principle impracticable for selecting support features from a large number of data. Introducing the notion of implicit SC-based feature selection, this paper presents a feature selection procedure that is equivalent to the standard SC-based feature selection procedure in supervised learning but with the memory requirement that is multiple orders of magnitude less than the counterpart. With experiments on six large machine learning datasets, we demonstrate the usefulness of the proposed implicit SCbased feature selection scheme in large-scale supervised data analysis.

Original languageEnglish
Title of host publicationAlgorithmic Aspects in Information and Management - Third International Conference, AAIM 2007, Proceedings
Pages411-423
Number of pages13
Publication statusPublished - 2007 Dec 1
Event3rd International Conference on Algorithmic Aspects in Information and Management, AAIM 2007 - Portland, OR, United States
Duration: 2007 Jun 62007 Jun 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4508 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Algorithmic Aspects in Information and Management, AAIM 2007
CountryUnited States
CityPortland, OR
Period07/6/607/6/8

Keywords

  • Combinatorial optimization
  • Feature selection
  • Large datasets
  • Supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ryoo, H. S., & Jang, I. Y. (2007). A heuristic method for selecting support features from large datasets. In Algorithmic Aspects in Information and Management - Third International Conference, AAIM 2007, Proceedings (pp. 411-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4508 LNCS).