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Axiomatic approach to feature subset selection based on relevance

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3 Author(s)
Hui Wang ; Fac. of Inf., Ulster Univ., Newtownabbey, UK ; D. Bell ; F. Murtagh

Relevance has traditionally been linked with feature subset selection, but formalization of this link has not been attempted. In this paper, we propose two axioms for feature subset selection-sufficiency axiom and necessity axiom-based on which this link is formalized: The expected feature subset is the one which maximizes relevance. Finding the expected feature subset turns out to be NP-hard. We then devise a heuristic algorithm to find the expected subset which has a polynomial time complexity. The experimental results show that the algorithm finds good enough subset of features which, when presented to C4.5, results in better prediction accuracy

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:21 ,  Issue: 3 )