Evolving Feature Selection

Authors:
Huan Liu;Edward R. Dougherty;Jennifer G. Dy;Kari Torkkola;Eugene Tuv;Hanchuan Peng;Chris Ding;Fuhui Long;Michael Berens;Lance Parsons;Zheng Zhao;Lei Yu;George Forman
Affiliations:
Arizona State University;Texas A&M University;Northeastern University;Motorola;Intel;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Translational Genomics Research Institute;Arizona State University;Arizona State University;State University of New York, Binghamton;Hewlett-Packard Labs
Venue:
IEEE Intelligent Systems
Year:
2005

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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.This department is part of a special issue on Data Mining in Bioinformatics.