A hybrid method for dimensionality reduction in microarray data based on advanced binary ant colony algorithm

The advent and proliferation of high-dimensional data have drawn the attention of researchers toward the subject of feature selection in machine learning and data mining. Increased number of irrelevant and redundant features has decreased the accuracy of classifiers, increased their computational cost and reinforced the "curse of dimensionality". This paper proposes a hybrid method, where first a number of filter methods reduce the dimensionality of features and then the advanced binary ant colony (ABACOh) meta-heuristic algorithm runs on the set of reduced features to select the most effective feature subset. Performance of the proposed method is measured by the applying on the five well-known high-dimensional microarray datasets and the results are compared with those of several state-of-the-art methods. The obtained results confirm the effectiveness of the proposed algorithm.