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Table 4 Experimental results for each run using epso on leukemia2, lung_cancer, SRBCT prostate_tumor, and DLBCL data stes

From: An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

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
  1. 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.