Gene Selection from Microarray Data Using Binary Grey Wolf Algorithm for Classifying Acute Leukemia

Background: Microarray technologies provide huge amount of information and is particularly helpful in the prediction and diagnosis of cancer. To accurately classify cancers, genes related to cancer have to be selected, as genes mined from microarrays possess too much noise. Method: In the current work, new binary modifications of the Grey Wolf Optimization (GWO) is suggested for choosing optimal features subsets for classification. In the proposed approach, GWO is modified by binarizing only the initial three optimal solutions and updation of the wolf position using stochastic crossover. Modification was also carried out using sigmoidal functions to compress the continuous updated positions. Multilayer Perceptron – Neural Network (MLP-NN) classifier is used for classifying the selected features. Results: The ALL/AML Leukemia dataset is used for evaluating Markov Blanket filter, minimum Redundancy Maximum Relevance (mRMR), Binary GWO and Mutated Binary GWO (MBGWO) with regard to classification accuracy, sensitivity and specificity. The proposed MBGWO achieved classification accuracy of 95.45% and also has better sensitivity and specificity. Conclusion: Experiments reveal the capabilities of the proposed MBGWO to explore features space for the optimal features combination for gene selection from microarray data.