Table 3: Filter feature selection methos (non-soft computing methods)

Name of the Method Parametric Non-Parametric
Univariate ANOVA [94] Y N
Fold-change [91] Y N
Regression model [92] Y N
Regularized t-test [95] Y N
Linear Model for Microarray Data(LIMMA) [96] Y N
Gene ranking with B-statistics [97] Y N
Gamma model [90, 93, 98, 120] Y N
Signal to noise ratio [118] Y N
Rank-sum [99] N Y
Rank product [100] N Y
Between-Within class Sum of Squares(BWSS) [101] N Y
Relative entropy [102] N Y
Threshold number of Misclassification(TnoM) [103] N Y
Area Between the Curve and the Rising diagonal(ABCR) [104] N Y
Significance Analysis of Microarray [105] N Y
Empirical Bayes Analysis(EBA) [106] N Y
Mixture Model Method(MMM) [107] N Y
Bivariate Greedy t-test [108] Y N
All pair t-test [108], N Y
Top scoring pairs [109] N Y
Uncorrelated Shrunken Centroid (USC) [110] N Y
Multivariate Correlation based Feature Selection(CFS) [111] N Y
Minimum Redundancy Maximum Relevance(MRMR) [112] N Y
Markov Blanket Filter(MBF) [113] N Y