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Advances in Bioinformatics
Volume 2016, Article ID 1058305, 16 pages
http://dx.doi.org/10.1155/2016/1058305
Research Article

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

1Department of Computer Engineering, Imam Reza International University, Mashhad, Iran
2Department of Software Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran

Received 28 November 2015; Revised 20 February 2016; Accepted 22 February 2016

Academic Editor: Pietro H. Guzzi

Copyright © 2016 Atiyeh Mortazavi and Mohammad Hossein Moattar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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