A comparative study of suboptimal branch and bound algorithms

The branch and bound algorithm is widely known as an efficient approach for selecting optimal feature subsets. If the optimality of the solution is allowed to be compromised, it is possible to further improve the search speed of the branch and bound algorithm. This paper studies the look-ahead search strategy which can eliminate many solutions deemed to be suboptimal early in the branch and bound search. We propose ways to incorporate the look-ahead search scheme into four major branch and bound algorithms, namely the basic branch and bound algorithm, the ordered branch and bound algorithm, the fast branch and bound algorithm, and the adaptive branch and bound algorithm. A comparative study of the look-ahead scheme in terms of the computational cost and the solution quality on these suboptimal branch and bound algorithms is carried out on real data sets. Furthermore, we test the feasible use of suboptimal branch and bound algorithms on a high-dimensional data set and compare its performance with other well-known suboptimal feature selection algorithms.