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Table 3 Experimental result for each using run epso on 11_tumors, 9_tumors, brain_tumor1, brain_tumor2 and leukemia1 data sets

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

Run# 11_Tumors 9_Tumors Brain_Tumor1 Brain_Tumor2 Leukemia1
  #Acc (%) #Selected genes #Acc (%) #Selected genes #Acc (%) #Selected genes #Acc (%) #Selected genes #Acc (%) #Selected genes
1 96.55 243 76.67 251 93.33 8 94 4 100 3
2 95.98 245 76.67 255 93.33 11 94 5 100 4
3 95.98 250 75 231 92.22 6 94 8 100 3
4 95.40 232 75 237 92.22 7 92 4 100 3
5 95.40 241 75 242 92.22 8 92 7 100 3
6 95.40 244 75 253 92.22 9 92 7 100 4
7 94.83 218 75 255 92.22 11 92 7 100 4
8 94.83 229 75 261 91.11 3 92 7 100 3
9 94.83 232 73.33 238 91.11 5 92 8 100 3
10 94.83 243 73.33 248 91.11 7 90 3 100 2
Average ± S.D. 95.40 ±0.61 237.70 ±9.66 75 ±1.11 247.10 ±9.65 92.11 ±0.82 7.5 ±2.51 92.40 ±1.26 6.00 ±1.83 100.00 ±0 3.20 ±0.63
  1. Note: Results of the best subsets 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.