Evaluation of the branch and bound algorithm for feature selection

Abstract 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.