An improved feature selection algorithm based on graph clustering and ant colony optimization

Dimensionality reduction is an important preprocessing step to improve the performance of machine learning algorithms. Feature selection methods can efficiently speed up the learning process and improve the overall classification accuracy by reducing the computational complexity. Among the feature selection methods, multivariate methods are more effective in removing irrelevant and redundant features. An efficient multivariate feature selection method, optimization method, called 'graph clustering based ant colony optimization (GCACO)' has been recently introduced and shown to outperform other well-known feature selection methods. In the GCACO, features are divided into communities (clusters) in the entire feature space represented as a graph by an efficient community detection algorithm. An ACO-based search strategy is then used to select an optimal feature subset from the initial set of features. In this paper, a modified GCACO algorithm called MGCACO is presented to significantly improve the performance of the GCACO. Performance of the MGCACO algorithm was assessed by testing it on several standard benchmark datasets and sleep EEG data. The performance of the MGCACO was compared to those obtained using the original GCACO and other well-known filtering methods available in the literature. The MGCACO achieved superior performance over the GCACO and other univariate and multivariate algorithms with up to 10%. The MGCACO also exhibited higher efficiency in reducing the number of features all by keeping the classification accuracy maximum.