dynamic programming as applied to feature subset selection in a pattern recognition system
This paper proposes dynamic programming search procedures to expedite the feature subset selection processes in a pattern recognition system. It is shown that in general the proposed procedures require far less number of subsets to be evaluated than the exhaustive search procedure. For example, a problem of selecting the best subset of 4 features from a set of 24 features requires an evaluation of [equation] &equil; 10626 subsets by using the exhaustive search procedure; on the other hand, it requires only 175 and 136 subsets to be considered by employing the proposed procedures I and II, respectively, to solve the same problem. While the number of subsets to be evaluated for the dynamic programming search procedures is slightly greater than that for the without-replacement search procedure, the best feature subset selected by the proposed methods may, however, not necessarily contain all of the best single features selected in the previous stages.
dynamic programming feature subset selection pattern recognition search procedures return function transformation function multistage decision process feature ranking