A Binary Krill Herd Approach for Feature Selection

Meta-heuristic-based feature selection has been paramount in the last years, mainly because of its simplicity, effectiveness and also efficiency in some cases. Such approaches are based on the social dynamics of living organisms, and can vary from birds, bees, bats and ants. Very recently, an optimization algorithm based on krill herd (KH) was proposed for continuous-valued applications, and it has been more accurate than some state-of-the-art techniques. In this paper, we propose a binary optimization version of KH technique, and we validate it for feature selection purposes in several datasets. The experiments showed the proposed technique outperforms three other meta-heuristic-based approaches for this task, being also so fast as the compared techniques.