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International Conference on Algorithmic Applications in Management

AAIM 2007: Algorithmic Aspects in Information and Management pp 411–423Cite as

A Heuristic Method for Selecting Support Features from Large Datasets

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4508))

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 SC-based feature selection scheme in large-scale supervised data analysis.

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2005-003-D00445).

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Authors and Affiliations

  1. Division of Information Management Engineering, Korea university, 1, 5-Ka, Anam-Dong, Seongbuk-Ku, Seoul, 136-713, Korea

    Hong Seo Ryoo & In-Yong Jang

Authors
  1. Hong Seo Ryoo
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  2. In-Yong Jang
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Ming-Yang Kao Xiang-Yang Li

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© 2007 Springer Berlin Heidelberg

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Ryoo, H.S., Jang, IY. (2007). A Heuristic Method for Selecting Support Features from Large Datasets. In: Kao, MY., Li, XY. (eds) Algorithmic Aspects in Information and Management. AAIM 2007. Lecture Notes in Computer Science, vol 4508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72870-2_39

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  • DOI: https://doi.org/10.1007/978-3-540-72870-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72868-9

  • Online ISBN: 978-3-540-72870-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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