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Finding rough set reducts with SAT. (English) Zbl 1134.68538

Ślȩzak, Dominik (ed.) et al., Rough sets, fuzzy sets, data mining, and granular computing. 10th international conference, RSFDGrC 2005, Regina, Canada, August 31 – September 3, 2005. Proceedings, Part I. Berlin: Springer (ISBN 3-540-28653-5/pbk). Lecture Notes in Computer Science 3641. Lecture Notes in Artificial Intelligence, 194-203 (2005).
Summary: 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 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 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, minimal subsets can be located and verified. An initial experimental investigation is conducted, comparing the new method with a standard rough set-based feature selector.
For the entire collection see [Zbl 1086.68007].

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
68T05 Learning and adaptive systems in artificial intelligence
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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