Feature Selection based on Particle Swarm Optimization and Mutual Information

Today, feature selection, as a technique to improve the performance of the classification methods, has been widely considered by the computer scientists. As the dimensions of a matrix has a huge impact on the performance of processing on it, reducing the number of features by choosing the best subset of all the features. It will affect the performance of the algorithms. Finding the best subset by comparing all the possible subsets, even when n is small, is an intractable process, and hence, many research works have approached to the heuristic methods to find a near-optimal solutions. In this paper, we introduce a novel feature selection technique that selects the most informative features and omits the redundant or irrelevant ones. Our method is embedded in PSO (Particle Swarm Optimization). In order to omit the redundant or irrelevant features, it is necessary to figure out the relationship between different features. There are many correlation functions that can reveal this relationship. In our proposed method, to find this relationship, we use the mutual information technique. We evaluate the performance of our method on three classification benchmarks: Glass, Vowel, and Wine. Comparing the results obtained with four state-of-the-art methods demonstrates its superiority over them.