A Modified Artificial Bee Colony Algorithm-Based Feature Selection for the Classification of High-Dimensional Data

In the real-world pattern recognition applications, the feature vectors and the classifier are two important factors for improving the classification accuracy. This paper presents a new approach that is based on the modified artificial bee colony (ABC) algorithm and the support vector machine (SVM) classifier to select the optimal feature subset. To improve the convergence of ABC, the modified ABC algorithm (named OGR-ABC algorithm) introduces three modified strategies including opposite initialization, global optimum based search equations and ranking based selection mechanism. Several UCI datasets have been used to evaluate the performance of feature selection by using the proposed OGR-ABC algorithm and SVM. The experiment results show that the proposed approach can achieve higher classification accuracy and more appropriate feature subset than the original ABC, genetic algorithm, and particle swarm optimization.