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. 2013 Apr 24;8:15. doi: 10.1186/1748-7188-8-15

Table 4.

Experimental results for each run using epso on leukemia2, lung_cancer, SRBCT prostate_tumor, and DLBCL data stes

Run#
Leukemia2
Lung_Cancer
SRBCT
Prostate_Tumor
DLBCL
  #Acc (%) #Selected genes #Acc (%) #Selected genes #Acc (%) #Selected genes #Acc (%) #Selected genes #Acc (%) #Selected genes
1
100
4
96.06
7
100
27
99.02
5
100
3
2
100
4
96.06
10
100
11
98.04
4
100
4
3
100
5
96.06
12
100
12
98.04
6
100
4
4
100
6
95.57
6
98.80
8
98.04
8
100
5
5
100
7
95.57
7
98.80
9
98.04
8
100
5
6
100
7
95.57
7
100
48
98.04
11
100
5
7
100
7
95.57
8
98.80
7
98.04
8
100
5
8
100
8
95.57
9
100
12
97.06
4
100
5
9
100
9
95.57
11
100
8
97.06
6
100
5
10
100
11
95.07
6
100
7
97.06
6
100
6
Average ± S.D. 100.00 ±0 6.80 ±2.20 95.67 ±0.31 8.30 ±2.11 99.64 ±0.58 14.90 ±13.03 97.84 ±0.62 6.60 ±2.17 100.00 ±0 4.70 ±0.82

Note: Results of the best subsets shown are written in a bold style. A near-optimal subset that produces the highest classification accuracy with the smallest number of genes is selected as the best subset. #Acc and S.D. denote the classification accuracy and the standard deviation, respectively, whereas #Selected Genes and Run# represent the number of selected genes and a run number, respectively.