Gaussian Transformation Based Representation in Particle Swarm Optimisation for Feature Selection

In classification, feature selection is an important but challenging task, which requires a powerful search technique. Particle swarm optimisation (PSO) has recently gained much attention for solving feature selection problems, but the current representation typically forms a high-dimensional search space. A new representation based on feature clusters was recently proposed to reduce the dimensionality and improve the performance, but it does not form a smooth fitness landscape, which may limit the performance of PSO. This paper proposes a new Gaussian based transformation rule for interpreting a particle as a feature subset, which is combined with the feature cluster based representation to develop a new PSO-based feature selection algorithm. The proposed algorithm is examined and compared with two recent PSO-based algorithms, where the first uses a Gaussian based updating mechanism and the conventional representation, and the second uses the feature cluster representation without using Gaussian distribution. Experiments on commonly used datasets of varying difficulty show that the proposed algorithm achieves better performance than the other two algorithms in terms of the classification performance and the number of features in both the training sets and the test sets. Further analyses show that the Gaussian transformation rule improves the stability, i.e. selecting similar features in different independent runs and almost always selects the most important features.