Study on criterion function models in the adaptive branch and bound algorithm

The adaptive branch and bound algorithm was recently introduced to accelerate the search speed for optimal feature selection. The algorithm improves upon prior branch and bound algorithms in many aspects. One of the major improvements is to model the criterion function as a simple mathematical function and to adapt it in the proposed jump search strategy to avoid redundant computations. In this paper, we investigate various mathematical functions that can be used as the criterion function model. Experimental results for two real data sets demonstrate that other simple criterion function models perform as well as the default one.