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

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.

A Novel Dimensionality Reduction Approach to Improve Microarray Data Classification

“…The major aim behind this combination is to use a bare minimum of dimensions while maximizing the classification performance. Regarding Table 5, the most interesting result concerned the Leukemia1 dataset since the best model, P-PCA-C5.0, achieved an excellent accuracy of 100% with only ′ = 3 selected dimensions and a response time of 2 seconds, which is much better than what was reported in [11][12], [14][15]. The classification accuracy of 100% was obtained by computing the percentage of correct predictions from the confusion matrix (c) shown in Fig.…”

“…Atiyeh and Mohammad implemented an innovative feature selection approach Based on Cooperative Game Theory and Qualitative Mutual Information (QMT). The classification accuracy on 11 microarray datasets, namely Leukemia1, SRBCT, Lung, and prostate cancer, shows that the proposed approach improves both accuracy and stability compared to other methods [12]. Chandra proposed an efficient feature selection technique that removes the drawbacks of [13], by taking into account the redundancy between features.…”

Loan Prepayment Prediction Based on SVM-RFE and XGBoost Models

“…Then for the optimal solution of the objective function, only the second order can be considered and the expression is shown as Eq. (5).…”

“…Chehong Zhu [4] developed and designed a deep learning entity model of mortgage risk, and found that there is a highly optimal control relationship between personal behavior of early repayment of loans and loan characteristics and macroeconomic variables. Mortazavi A [5] removed irrelevant features by searching the optimal feature subset to produce the minimum error on the original dataset. Schapiro et al [6] proposed the AdaBoost algorithm, which allows the classifier to improve its focus on a small number of classes of samples by focusing on the samples that are misclassified after each round and giving them higher weights.…”