The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of\ncancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high\ndimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new\nfeature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization\n(SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm\n(GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method\ncontains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS),\nsupport vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support\nvector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature\nsubsets are used to train the SVM classifier for cancer classification.We also use random forest feature selection (RFFS), random\nforest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature\nselection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained\nby feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC)\nin the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful\ngenes.
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