Table 9: Nonsoft computing methods performance analysis for microarray data.

Name of the Method Key Idea Performance Analysis
Euclidian distance and Pearson correlation feature selection [66] • 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 [65], t-test [121] • 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 [63] • 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 [72] • 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) [74] • 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) [80] • 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.