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Evaluation of the branch and bound algorithm for feature selection. (English) Zbl 0941.68722

Summary: Narendra and Fukunaga show that the branch and bound algorithm guarantees the optimal feature subset without evaluating all possible feature subsets, if the criterion function used satisfies the ‘monotonicity’ property. In this paper, we show that the algorithm works well in terms of the recognition rate, even if the property is not satisfied.

MSC:

68T10 Pattern recognition, speech recognition
90C57 Polyhedral combinatorics, branch-and-bound, branch-and-cut
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