Serial filter-wrapper feature selection method with elite guided mutation strategy on cancer gene expression data

Nowadays, many researchers utilize cancer gene expression data to solve the problem of cancer subtype diagnosis, but cancer gene expression data are often high-dimensional, multi-sample, and multi-classified, so a hybrid serial filter-wrapper feature selection (FS) method based on elite guided mutation strategy for cancer gene expression data is proposed. It is divided into a preliminary screening phase and a combined modeling phase. In the preliminary screening stage, the threshold values of seven filter methods are determined by the leave-one cross-validation method, and the features selected by these seven filter methods are combined to form two subsets by using the thoughts of ''And'' and ''Or'' in the logical operation. The union subset of two subsets is used in the equilibrium optimizer (EO) in the subsequent combination model stage as the reserved subset in the preliminary screening stage. The resulting hybrid framework is connected by a parallel filter method designed in the first stage with an improved EO in the second stage, which is named as SFEMEO. In order to prove the effectiveness and generalization of the proposed SFEMEO, it is compared with other 9 basic algorithms on 10 UCI data sets. It is found that the classification accuracy of the SFEMEO is improved by 0.56% similar to 20.19%, and the optimal fitness is also greatly improved. After comparing SFEMEO with other nine intelligent optimization algorithms on ten cancer gene expression data sets, it can be found that compared with most algorithms, the accuracy rate is improved by 3.73% similar to 18.13%, and the optimal fitness is relatively superior. At the same time, Wilcoxon rank sum test was used to evaluate the results of intelligent optimization algorithms such as SFEMEO, which proved the effectiveness of the proposed hybrid framework and its superiority in solving the FS problem of high-dimensional cancer gene expression data.