|Name of the Method||Key Idea||Performance Analysis|
|Euclidian distance and Pearson correlation feature selection ||• Euclidian distance and Pearson correlation coefficient for feature selection
• SVM classifier with different kernel
|Distance-based method outperforms for SVM with a linear kernel.|
|SNR , t-test ||• k-means for attribute clustering
• Signal to noise ratio and t-statistics for feature selection
• SVM, kNN, PNN, FNN are used for classification.
|The performance of SVM classifier gives a better result for the 5 features using k-means-SNR and k-means-t-test approach.|
|Filter approach ||• IG, RA, TA, and PCA for feature selection.
• SVM, kNN, DT, NB, and NN for classification.
|The best classification accuracy is achieved by using a subset of 250 features chosen by IG based method for SVM classifier.|
|mRMR ||• mRMR for feature selection
• NB, SVM, LDA for classification
|The computational cost of mRMR is low and the classification accuracy is high in comparison to maximum dependency and maximum relevance for all datasets.|
|Best Incremental Ranks Subset(BIRS) ||• BIRS (wrapper), nonlinear correlation measure based entropy and IG feature selection methods.
• NB,IB,C4 for classification
|The computational complexity of BIRS is better in comparison to CFS, FCBF.|
|Kernel panelized SVM(KP-SVM) ||• KP-SVM for feature selection
• SVM for classification
|The advantage of KP-SVM in terms of computational effort is that it automatically obtains an optimal feature subset, avoiding a validation step to determine how many ranked features will be used for classification.|