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Issue No. 06 - November/December (2005 vol. 20)
ISSN: 1541-1672
pp: 64-76
Huan Liu , Arizona State University
Edward R. Dougherty , Texas A&M University
Jennifer G. Dy , Northeastern University
Kari Torkkola , Motorola
Eugene Tuv , Intel
Hanchuan Peng , Lawrence Berkeley National Laboratory
Chris Ding , Lawrence Berkeley National Laboratory
Fuhui Long , Lawrence Berkeley National Laboratory
Michael Berens , Translational Genomics Research Institute
Lance Parsons , Arizona State University
Zheng Zhao , Arizona State University
Lei Yu , State University of New York, Binghamton
George Forman , Hewlett-Packard Labs
ABSTRACT
Feature selection is a preprocessing technique, commonly used on high-dimensional data, that studies how to select a subset or list of attributes or variables that are used to construct models describing data. Wide data sets, which have a huge number of features but relatively few instances, introduce a novel challenge to feature selection. This installment of Trends & Controversies looks at several different ways of meeting this challenge.<p>This department is part of a special issue on Data Mining in Bioinformatics.</p>
INDEX TERMS
feature selection, data mining, bioinformatics, text mining, clustering, classification
CITATION

L. Parsons et al., "Evolving Feature Selection," in IEEE Intelligent Systems, vol. 20, no. , pp. 64-76, 2005.
doi:10.1109/MIS.2005.105
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