Bidirectional ant colony optimization for feature selection

Feature selection is the process of choosing a subset of relevant as well as irredundant features from a bigger set. In other words, it removes redundant and irrelevant features from original set. In this paper, a new algorithm which is called bidirectional ant colony optimization feature selection (BDACOFS) based on ant colony optimization (ACO) algorithm and inspired from ACOFS (a recently proposed feature selection method) is presented. In the proposed algorithm, problem is modeled by a circular graph in which every node has only two arcs to its subsequent node. One of arcs represents selecting and another implies deselecting the next node. In addition, heuristic desirability of every node´s selection is calculated according to two factors; one is related to discrimination ability of features and second one is related to mutual information among features. The proposed algorithm has been tested against some well-known datasets and its performance has been compared to some well-known algorithms. The result indicates that proposed algorithm by adding mutual statistical information to its heuristic desirability could remove more redundant features than original ACOFS. Meanwhile it keeps classification accuracy as highly as the original ACOFS.