期刊
INFORMATION SCIENCES
卷 255, 期 -, 页码 100-120出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.07.033
关键词
Rough set theory; Fuzzy rough set theory; Feature selection; Boolean satisfiability
Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would otherwise be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding globally minimal reductions, the smallest sets of features possible. This paper proposes a technique that considers this problem from a propositional satisfiability perspective. In this framework, globally minimal subsets can be located and verified. (C) 2013 Elsevier Inc. All rights reserved.
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