The high-dimensionality of microarray data with small number of samples has presented a difficult challenge for the microarray data classification task. The aim of gene selection is to reduce the dimensionality of microarray data in order to enhance the accuracy of the classification task. Existing gene selection methods generally use class labels of the data while due to availability of mislabels or unreliable labels of samples in the microarray data, unsupervised methods could be more essential to the gene selection process. In this paper, we propose an unsupervised gene selection method called MGSACO, which incorporates the ant colony optimization algorithm into the filter approach, by minimizing the redundancy between genes and maximizing the relevance of genes. Moreover, a new fitness function is applied in the proposed method which does not need any learning model to evaluate the subsets of selected genes. Thus, it is classified into the filter approach. The performance of the proposed method is extensively tested up on five publicly available microarray datasets, and it is compared to those of the seven well-known unsupervised and supervised gene selection methods in terms of classification error rates of the three frequently used classifiers including support vector machine, naïve Bayes, and decision tree. Experimental results show that MGSACO is significantly superior to the existing methods over different classifiers and datasets.
