  In this paper, we present a new wrapper feature selection approach based on
Jensen-Shannon (JS) divergence, termed feature selection with maximum
JS-divergence (FSMJ), for text categorization. Unlike most existing feature
selection approaches, the proposed FSMJ approach is based on real-valued
features which provide more information for discrimination than binary-valued
features used in conventional approaches. We show that the FSMJ is a greedy
approach and the JS-divergence monotonically increases when more features are
selected. We conduct several experiments on real-life data sets, compared with
the state-of-the-art feature selection approaches for text categorization. The
superior performance of the proposed FSMJ approach demonstrates its
effectiveness and further indicates its wide potential applications on data
mining.
