B-VPL: A Binary Volleyball Premier League optimization algorithm for Feature Selection

This paper presents a binary version of the volleyball premier league optimization algorithm. It is applied in a feature selection problem where it searches for the more useful features. The proposed algorithm uses S-shaped and V-shaped transfer functions. This approach is compared with another meta-heuristic, binary particle swarm optimization in a Parkinson dataset. The learning algorithm utilized is k – nearest neighbor technique based on a wrapper approach. The results show that the binary volleyball premier league algorithm converges faster to the optimum, and it can be considered adequate in feature selection.