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. Author manuscript; available in PMC: 2015 Apr 1.
Published in final edited form as: Curr Bioinform. 2014 Apr 1;9(2):140–146. doi: 10.2174/1574893608999140109115649

Table 1.

Comparison of classification accuracy (%)

Method
Dataset
SGC-t SGC-W TGC-1 TGC-2 TSP DLDA k-NN SVM RF
Melanoma [44] 97 96 97 96 99 97 97 97 97
Breast Cancer 1 [45] 63 69 64 64 75 61 53 52 43
Brain Cancer [7] 80 77 77 75 77 65 73 60 70
Breast Cancer 2 [46] 58 50 82 78 47 73 67 73 67
Gastric Tumor [47] 89 80 89 88 91 81 96 97 95
Lung Cancer 1 [48] 98 95 98 100 95 95 98 98 98
Lung Cancer 2 [3] 93 93 93 93 97 99 99 99 99
Lymphoma [8] 74 71 59 60 57 66 52 59 57
Myeloma [49] 68 67 68 54 71 75 78 74 79
Pancreatic Cancer [50] 69 90 71 73 90 63 61 65 55
Prostate Cancer [6] 89 89 89 90 81 78 93 93 93

Note:

1 SGC-t: Single Gene Classifier with the t-test gene selection method.

2 SGC-W: Single Gene Classifier with the WMW gene selection method.

3 TGC-1: Two Gene Classifier Type 1.

4 TGC-1: Two Gene Classifier Type 2.

5 TSP: Top-Scoring Pair(s) (TSP) classifier.

6 DLDA: Diagonal Linear Discriminant Analysis.

7 k-NN: k-Nearest Neighbor (k=3).

8 SVM: Support Vector Machines.

9 RF: Random Forest.

10 Leave-one-out cross validation (LOOCV) results are presented.