Table 6.
Comparison of SVM, 1NN, MWIS-1NN, and MWIS-ACO-LS (LOOCV).
| Datasets | Performance | SVM | LR-L1 | Avg | Best | LR-Elasticnet | Avg | 1NN | MWIS | MWIS-ACO | MWIS-ACO-LS | Best |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11_Tumors | Accuracy (%) | 85.63 | 88.22 | 93.1 | 86.38 | 88,22 | 74,14 | 67,24 | 94,90 | 96 | 99,14 | 99,42 |
| Genes | 12533 | — | — | — | — | 12533 | 1308 | 463,00 | 460 | 166,9 | 101 | |
| Time (min) | — | — | — | — | — | — | 0,82 | 91,33 | — | 123,2 | — | |
|
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| 9_Tumors | Accuracy (%) | 38.33 | 35.00 | 50.00 | 29.50 | 38,33 | 40,00 | 60,00 | 98,83 | 100 | 100,00 | 100,00 |
| Genes | 5726 | — | — | — | — | 5726 | 263 | 90,10 | 83 | 51 | 40 | |
| Time (min) | — | — | — | — | — | — | 0,34 | 21,3 | — | 34,48 | — | |
|
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| Brain_Tumor1 | Accuracy (%) | 88.89 | 85.67 | 88.89 | 85.44 | 88,89 | 85,56 | 80,00 | 96,56 | 100,00 | 99,22 | 100,00 |
| Genes | 5920 | — | — | — | — | 5920 | 246 | 55,90 | 46 | 22,9 | 19 | |
| Time (min) | — | — | — | — | — | — | 0,22 | 29,14 | — | 45,81 | — | |
|
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| Brain_Tumor2 | Accuracy (%) | 70.00 | 27.20 | 32.00 | 29.20 | 36,00 | 60,00 | 48,00 | 95,40 | 100,00 | 99,40 | 100,00 |
| Genes | 10367 | — | — | — | — | 10367 | 110 | 22,40 | 18 | 11,1 | 11 | |
| Time (min) | — | — | — | — | — | — | 0,55 | 17,89 | — | 27,13 | — | |
|
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| Leukemia1 | Accuracy (%) | 97.22 | 91.81 | 94.44 | 92.64 | 95,83 | 83,33 | 66,67 | 100,00 | 100,00 | 100,00 | 100,00 |
| Genes | 5327 | — | — | — | — | 5327 | 297 | 63,00 | 56 | 9,4 | 5 | |
| Time (min) | — | — | — | — | — | — | 0,26 | 25,94 | — | 43,77 | — | |
|
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| Leukemia2 | Accuracy (%) | 97.22 | 91.81 | 95,83 | 91.67 | 95.83 | 86.11 | 73.61 | 100.00 | 100.00 | 100.00 | 100.00 |
| Genes | 11225 | — | — | — | — | 11225 | 203 | 45.80 | 42 | 13.9 | 11 | |
| Time (min) | — | — | — | — | — | — | 0,44 | 23,85 | — | 37.67 | — | |
|
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| Lung_Cancer | Accuracy (%) | 95.07 | 91.67 | 94.10 | 91.03 | 93.60 | 87.68 | 90.15 | 98.42 | 99.01 | 98.92 | 99.51 |
| Genes | 12600 | — | — | — | — | 12600 | 602 | 180,00 | 183 | 34,8 | 36 | |
| Time (min) | — | — | — | — | — | — | 0,82 | 74,61 | — | 107,3 | — | |
|
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| SRBCT | Accuracy (%) | 100.00 | 97.59 | 100.00 | 97.10 | 98.79 | 85,54 | 91,57 | 100,00 | 100,00 | 100,00 | 100,00 |
| Genes | 2308 | — | — | — | — | 2308 | 109 | 15,60 | 15 | 7,6 | 6 | |
| Time (min) | — | — | — | — | — | — | 0,14 | 23,48 | — | 38,24 | — | |
|
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| Prostate_Tumor | Accuracy (%) | 92.16 | 81.47 | 88.23 | 80.69 | 83.33 | 82,35 | 79,41 | 98,24 | 99,04 | 99,12 | 100,00 |
| Genes | 10509 | — | — | — | — | 10509 | 193 | 47,30 | 43 | 20,3 | 21 | |
| Time (min) | — | — | — | — | — | — | 0,39 | 29,2 | — | 46,59 | — | |
|
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| DLBCL | Accuracy (%) | 97.40 | 91.95 | 97.40 | 91.43 | 94.80 | 84,42 | 87,01 | 100,00 | 100,00 | 100,00 | 100,00 |
| Genes | 5469 | — | — | — | — | 5469 | 147 | 25,7 | 22 | 7,2 | 6 | |
| Time (min) | — | — | — | — | — | — | 0,17 | 27,18 | — | 43,63 | — | |
Note: the best results are shown in bold. Remark: as the SVM, 1NN, and MWIS are of deterministic nature, the classification is calculated just in one run. Accuracy: the classification accuracy using LOOCV (leave-one-out-cross-validation). Genes: the number of genes used in the classification ofthe LR-L1 and LR-Elasticnet methods. Best: the best result found in all ten runs. Avg: the average of the ten experiments. Time: the execution time in minutes. SVM: the support vector machine classifier using a linear kernel. LR-L1: the logistic regression classifier with the lasso regularisation. LR-Elasticnet: the logistic regression classifier with the elastic net regularisation. 1NN: the 1-nearest neighbor classifier. MWIS: the maximum weight independent set for gene selection. MWIS-ACO: our method of selection combining MWIS and ACO without using LS. MWIS-ACO-LS: our improved method of selection combining MWIS and ACO and the local search algorithm (LS).