Name of the Method | Key Idea | Performance Analysis |
---|---|---|
A hybrid approach with GA wrapper [48] | ▪ Ensemble feature selection using entropy-based feature ranking, t-statistics, and SVM-REF ▪ GA is applied to search an optimal or near optimal feature subset from the feature pool. ▪ SVM for classification |
SVM-RFE shows better classification performance than other selection techniques for all datasets. |
Redundant gene selection using PSO [122] | ▪ Gene selection by RGS-PSO ▪ SVM,LG,C45,kNN,NB are used for classification |
RGS-PSO and mRMR with 20genes are the best two methods, which have the top averaged BACC scores 0.818. |
Rough set and SVM based [123] | ▪ Rough set and MRMS for gene selection ▪ SVM for classification |
The MRMS selects a set of miRNAs having a lowest B.632+ bootstrap error rate of the SVM classifier for all the data sets. The better performance of the MRMS algorithm is achieved due to the use of rough sets. |
ACO [52] | ▪ ACO for feature selection ▪ In this paper, each gene is viewed as a node on the TSP problem. The nodes on the tour generated by the ant colony are the selected genes for cancer classification. ▪ BPNN for classification . |
ACO feature selection algorithm improves the performance of BPNN. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. |
Rough set based [128] | ▪ Rough set theory for feature selection by maximizing the relevance and significance of selected genes. ▪ K-NN and SVM for classification |
The performance of proposed MRMS criterion is better than that of Max-Dependency and Max-Relevance criteria in most of the cases. Out of total 28 cases, the MRMS criterion achieves significantly better results than Max-Dependency or Max-Relevance in 25 cases. |