Best first strategy for feature selection
Lei Xu, Pingfan Yan, Tong Chang · 2003
A heuristic search strategy taken from the field of artificial intelligence is applied to feature selection. An algorithm called BFF for feature selection is proposed. It is proved that this algorithm can guarantee the globally best subset without exhaustive enumeration for any criterion that satisfies monotonicity. It is shown that the number of subsets evaluated by BFF is less (even much less) than that needed by the branch and bound algorithm, an optimal feature selection algorithm proposed by P.M. Marendra and K. Funkunaga (1977).>