MF-GARF: Hybridizing Multiple Filters and GA Wrapper for Feature Selection of Microarray Cancer Datasets

DNA Microarray technology is a valuable advancement in medical field but it gives birth to many challenges like curse of dimensionality, storage and computational requirements. In this paper we have proposed, a multiple filters and GA wrapper based hybrid approach (MF-GARF) that incorporates Random forest as fitness evaluator of features. The proposed hybrid approach MF-GARF is comprised of three phases relevancy block; containing information theory based filters Information Gain, Gain Ratio and Gini Index, responsible for ensuring relevancy and removal of irrelevant and noisy features. Second phase is Redundancy block; incorporating Pearson Correlation statistics to remove redundancy among features, and then final phase Optimization Block; containing Genetic Algorithm wrapper with Random Forest as fitness evaluator, responsible for generating an optimal feature subset with high predictive power. Random Forest with 10-fold cross validation is used to calculate the classification accuracy of selected feature subset. Experiments are carried out on 7 publically available benchmark Microarray cancer datasets and the proposed algorithm has achieved good accuracy with minimal selected features for all datasets. The comparison with other state of the art hybrid techniques validates the effectiveness of our proposed approach.