Skip to main content

Table 6 A comparison between our method (EPSO) and previous PSO-based methods

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

Data

Method

EPSO [This work]

IBPSO[6]

PSOTS[3]

PSOGA[7]

TS-BPSO[8]

BPSO-CGA[9]

 

Evaluation

      

11_Tumors

Average #Acc (%)

95.40

-

-

-

-

-

Best #Acc (%)

96.55

93.10

-

-

97.35

-

Average #Genes

237.70

-

-

-

-

-

Best #Genes

243

2948

-

-

3206

-

9_Tumors

Average #Acc (%)

75

-

-

-

-

-

Best #Acc (%)

76.67

78.33

-

-

81.63

-

Average #Genes

247.10

-

-

-

-

-

Best #Genes

251

1280

-

-

2941

-

Brain_Tumor1

Average #Acc (%)

92.11

-

-

-

-

 

Best #Acc (%)

93.33

94.44

-

-

95.89

91.4

Average #Genes

7.5

-

-

-

-

-

Best #Genes

8

754

-

-

2913

456

Brain_Tumor2

Average #Acc (%)

92.4

-

-

-

-

-

Best #Acc (%)

94

94.00

-

-

92.65

-

Average #Genes

6.0

-

-

-

-

-

Best #Genes

4

1197

-

-

5086

-

Leukemia1

Average #Acc (%)

100

-

98.61

95.10

-

-

Best #Acc (%)

100

100

-

-

100

100

Average #Genes

3.2

-

7

21

-

-

Best #Genes

2

1034

-

-

2577

300

Leukemia2

Average #Acc (%)

100

-

-

-

-

-

Best #Acc (%)

100

100

-

-

100

-

Average #Genes

6.8

-

-

-

-

-

Best #Genes

4

1292

-

-

5609

-

Lung_Cancer

Average #Acc (%)

95.67

-

-

-

-

-

Best #Acc (%)

96.06

96.55

-

-

99.52

-

Average #Genes

8.3

-

-

-

-

-

Best #Genes

7

1897

-

-

6958

-

SRBCT

Average #Acc (%)

99.64

-

-

-

-

-

Best #Acc (%)

100

100

-

-

100.00

100

Average #Genes

14.90

-

-

-

-

-

Best #Genes

7

431

-

-

1084

880

Prostate_Tumor

Average #Acc (%)

97.84

-

-

-

-

-

Best #Acc (%)

99.02

92.16

-

-

95.45

93.7

Average #Genes

6.6

-

-

-

-

-

Best #Genes

5

1294

-

-

5320

795

DLBCL

Average #Acc (%)

100

-

-

-

-

-

Best #Acc (%)

100

100

-

-

100.00

-

Average #Genes

4.7

-

-

-

-

-

 

Best #Genes

3

1042

-

-

2671

-

  1. Note: The best result of each evaluation is written in a bold style. The best result of evaluations could not be compared and stated if the results of evaluations have not been reported in the previous works. The best method of each data set is shown in the shaded cells. The best method is selected based on the highest number of the best results for all evaluations. #Acc and S.D. denote the classification accuracy and the standard deviation, respectively, whereas #Genes and #Ave represent the number of selected genes and an average, respectively. #Time stands for running time in the hour unit. ‘-‘ means that a result is not reported in the previous related work.
  2. IBPSO = An improved binary PSO. PSOTS = A hybrid of PSO and tabu search.
  3. PSOGA = A hybrid of PSO and GA. TS-BPSO = A combination of tabu search and BPSO.
  4. BPSO-CGA = A hybrid of BPSO and a combat genetic algorithm.
Back to article page

Algorithms for Molecular Biology

ISSN: 1748-7188