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The Minimum Feature Subset Selection Problem

Chen Bin; Hong Jiarong; Wang Yadong;   

  1. Department of Computer Science; Harbin Institute of Technology; Harbin 150001;
  • Online:1997-03-10 Published:1997-03-10

In applications of learning from examples to real-world tasks, feature subset selection is important to speed up training and to improve generalization performance. ideally, an inductive algorithm should use subset of features as small as possible. In this paper however, the authors show that the problem of selecting the minimum subset of features is NP-hard. The paper then presents a greedy algorithm for feature subset selection. The result of running the greedy algorithm on hand-written numeral recognitio…

Key words: object hierarchy and geometric transformation; feature representation; three-dimensional graphics and realism; system and information processing;

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[3] Caruana R, Freitag D. Greedy Attribute Selection. ML'94, Rutgers University, New Brunswick, NJ, pp.28-36, 1994. ……….
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