Fuzzy-rough feature selection using flock of starlings optimisation

Much use has been made of particle swarm optimisation as a tool to solve complex optimisation tasks, and many extensions and modifications to the original algorithm have been proposed. One such extension is related to the murmuration or flocking behaviour of starling birds and their flight trajectories in relation to flock cohesion giving rise to the so-called flock of starlings optimisation algorithm. This algorithm uses the topological model of starling bird flocks as a basis for modifying the original particle swarm optimisation approach. In this paper, two novel approaches for feature selection using fuzzy-rough sets and based upon two different interpretations of the flock of starlings algorithm are proposed. The results demonstrate that the approach can converge quickly and can discover subsets of smaller size and which are more stable than traditional PSO.