Surrogate-Model Based Particle Swarm Optimisation with Local Search for Feature Selection in Classification

Evolutionary computation (EC) techniques have been applied widely to many problems because of their powerful search ability. However, EC based algorithms are usually computationally intensive, especially with an expensive fitness function. In order to solve this issue, many surrogate models have been proposed to reduce the computation time by approximating the fitness function, but they are hardly applied to EC based feature selection. This paper develops a surrogate model for particle swarm optimisation based wrapper feature selection by selecting a small number of instances to create a surrogate training set. Furthermore, based on the surrogate model, we propose a sampling local search, which improves the current best solution by utilising information from the previous evolutionary iterations. Experiments on 10 datasets show that the surrogate training set can reduce the computation time without affecting the classification performance. Meanwhile the sampling local search results in a significantly smaller number of features, especially on large datasets. The combination of the two proposed ideas successfully reduces the number of features and achieves better performance than using all features, a recent sequential feature selection algorithm, original PSO, and PSO with one of them only on most datasets.