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<p><b>Abstract</b>—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 <it>expected feature subset</it> 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.</p>
Machine learning, knowledge discovery, feature subset selection, relevance, entropy.

H. Wang, F. Murtagh and D. Bell, "Axiomatic Approach to Feature Subset Selection Based on Relevance," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 271-277, 1999.
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