On the suboptimal solutions using the adaptive branch and bound algorithm for feature selection

Abstract

The branch and bound algorithm is an optimal feature selection method that is well-known for its computational efficiency. The recently developed adaptive branch and bound algorithm has been shown to be several times faster than other versions of the branch and bound algorithm. If the optimality of the algorithm is allowed to be compromised, we can further improve the search speed by employing the look-ahead search strategy to eliminate many solutions deemed to be suboptimal early in the search. We investigate the effects of this scheme on the computational cost and suboptimal solutions obtained using the adaptive branch and bound algorithm and compare them with those using the basic branch and bound algorithm. Our experimental results for two different databases demonstrate that by setting the look-ahead parameter to an appropriate value, we can significantly reduce the search time of the adaptive branch and bound algorithm while retaining its optimal solutions.