NDLI: Incremental Wrapper-based subset Selection with replacement: An advantageous alternative to sequential forward selection

Content Provider	IEEE Xplore Digital Library
Author	Bermejo, P. Gamez, J.A. Puerta, J.M.
Copyright Year	2009
Description	Author affiliation: Intelligent Systems and Data Mining group, Computing Systems Department / i3A Universidad de Castilla-La Mancha. Albacete, Spain (Bermejo, P.; Gamez, J.A.; Puerta, J.M.)
Abstract	This paper deals with the problem of wrapper-based feature subset selection in classification oriented datasets with a (very) large number of attributes. In such datasets sophisticated search algorithms like beam search, branch and bound, best first, genetic algorithms, etc., become intractable in the wrapper approach due to the high number of wrapper evaluations to be carried out. One way to alleviate this problem is to use the so-called filter-wrapper approach or Incremental Wrapper-based Subset Selection (IWSS), which consists in the construction of a ranking among the predictive attributes by using a filter measure, and then a wrapper approach is used guided by the rank. In this way the number of wrapper evaluations is linear with the number of predictive attributes. In this paper we present a contribution to the IWSS approach which helps it to obtain more compact subsets, and consists into allow not only the addition of new attributes but also the interchange with some of the already included in the selected subset. The disadvantage of this novelty is that it grows up the worst-case complexity of IWSS up to O(n2), however, as in the case of the well known sequential forward selection (SFS) the actual number of wrapper evaluations is considerably smaller. Empirical tests over 7 (biological) datasets with a large number of attributes demonstrate the success of the proposed approach when comparing with both IWSS and SFS.
Starting Page	367
Ending Page	374
File Size	405259
Page Count	8
File Format	PDF
ISBN	9781424427659
DOI	10.1109/CIDM.2009.4938673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency selective surfaces Filters Supervised learning Evolutionary computation Concrete Data mining Structural beams Intelligent systems Genetic algorithms Testing
Content Type	Text
Resource Type	Article

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