Rough set theory is a valid mathematical tool for dealing with the problem of feature subset selection. In this paper, to break the restriction of the conception of conditional entropy and provide an effective measurement to the relative importance of redundant features, system entropy of a decision system is defined based on rough set theory; some of its algebraic characteristics are also researched. Then two similar heuristic algorithms are introduced to select features based on the notion of system entropy. Moreover, different characteristics of the two proposed algorithms are also deeply analyzed and discussed. The two new algorithms may surely maintain the discernible relation of decision systems; their space and time complexities are obviously much lower than that of analogous algorithms in literature. Simulation results on numerous UCI machine-learning databases indicate that the optimal feature subsets may always be expected through the two algorithms on almost all cases.
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