A new feature selection algorithm based on binary ant colony optimization

Feature selection is an indispensable preprocessing step for effective analysis of high dimensional data. In this paper a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are treated as graph nodes to construct a graph model. In this graph, each feature has two nodes, one for selecting that feature and the other for deselecting. Ant colony algorithm is used to select nodes while ants should visit all features. At the end of a tour, each ant has a binary vector with the same length as the number of features where 1 implies selecting and 0 implies deselecting the corresponding feature. The experimental comparison verifies that the algorithm has a good classification accuracy using a smaller feature set than another existing ACO-based feature selection method.