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Table 9 A comprehensive comparison of all methods

From: FeatureSelect: a software for feature selection based on machine learning approaches

AL Learner = SVM Learner = ANN Learner = Decision tree
SEN SPC PRE FPR ACC SEN SPC PRE FPR ACC SEN SPC PRE FPR ACC
WCC 0/92 0/25 0/43 0/75 0/51 0/94 0/21 0/63 0/79 0/63 0/45 0/69 0/34 0/31 0/52
LCA 0/92 0/25 0/43 0/75 0/51 0/85 0/24 0/70 0/76 0/70 0/46 0/67 0/36 0/33 0/50
GA 0/92 0/25 0/43 0/75 0/51 0/96 0/02 0/63 0/98 0/63 0/44 0/61 0/33 0/39 0/45
PSO 0/92 0/25 0/43 0/75 0/51 1/00 0/00 0/65 1/00 0/65 0/44 0/63 0/31 0/37 0/47
ACO 0/92 0/25 0/43 0/75 0/51 0/97 0/14 0/72 0/86 0/72 0/43 0/60 0/31 0/40 0/43
ICA 0/92 0/25 0/43 0/75 0/51 1/00 0/00 0/70 1/00 0/70 0/44 0/62 0/33 0/38 0/45
LA 0/92 0/25 0/43 0/75 0/51 1/00 0/00 0/73 1/00 0/73 0/45 0/63 0/36 0/37 0/42
HTS 0/93 0/21 0/42 0/79 0/49 0/90 0/33 0/55 0/67 0/55 0/43 0/57 0/31 0/43 0/41
FOA 0/90 0/32 0/46 0/68 0/54 0/94 0/22 0/67 0/78 0/67 0/44 0/63 0/34 0/37 0/46
DSOS 0/92 0/25 0/43 0/75 0/51 0/74 0/51 0/67 0/49 0/67 0/44 0/61 0/34 0/39 0/44
CUK 0/92 0/25 0/43 0/75 0/51 0/83 0/40 0/65 0/60 0/65 0/43 0/59 0/28 0/41 0/43
PCRR 0/98 0/04 0/36 0/96 0/43 0/96 0/02 0/67 0/98 0/67 0/43 0/28 0/15 0/72 0/17
LAP 0/94 0/17 0/40 0/83 0/48 0/77 0/35 0/67 0/65 0/67 0/44 0/39 0/18 0/61 0/27
ENT 0/94 0/17 0/40 0/83 0/48 1/00 0/00 0/67 1 0/67 0/43 0/61 0/30 0/39 0/45
MI 1/00 0/00 0/35 1/00 0/41 1/00 0/00 0/68 1 0/68 0/50 0/00 0/00 1/00 0/00
Fisher 1/00 0/00 0/35 1/00 0/41 0/98 0/06 0/67 0/94 0/67 0/50 0/00 0/00 1/00 0/00
  1. Boldface values indicate the best-obtained results of each criterion for every learner