High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative\ngame theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension\nof microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual\ninformation and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation\nof the proposed approach is to employ Qualitative Mutual Information (QMI) for this purpose. The idea of Qualitative Mutual\nInformation causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance\nand scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The\nperformance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum\nredundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification\naccuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability\ncompared to other approaches.
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