Feature selection: near set approach

  • Authors:

  • James F. Peters;Sheela Ramanna

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada;Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada

  • Venue:
  • MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
  • Year:
  • 2007

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Abstract

The problem considered in this paper is how to select features that are useful in classifying perceptual objects that are qualitatively but not necessarily spatially near each other. The term qualitatively near is used here to mean closeness of descriptions or distinctive characteristics of objects. The solution to this problem is inspired by the work of Zdzisław Pawlak during the early 1980s on the classification of objects. In working toward a solution of the problem of the classification of perceptual objects, this article introduces a near set approach to feature selection. Consideration of the nearness of objects has recently led to the introduction of what are known as near sets, an optimist's view of the approximation of sets of objects that are more or less near each other. Near set theory started with the introduction of collections of partitions (families of neighbourhoods) that provide a basis for a feature selection method based on the information content of the partitions of a set of sample objects. A byproduct of the proposed approach is a feature filtering method that eliminates features that are less useful in the classification of objects. This contribution of this article is the introduction of a near set approach to feature selection.