Table 5.
Optimal FS methods for each dataset.
| FS accuracy % | FM | ES | SI | LR | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Feature selection method | CFS | ChiS | MRMR | WSA | CFS | ChiS | MRMR | WSA | CFS | ChiS | MRMR | WSA | CFS | ChiS | MRMR | WSA |
| Original no. of attributes | 74 | 74 | 74 | 74 | 68 | 68 | 68 | 68 | 88 | 88 | 88 | 88 | 75 | 75 | 75 | 75 |
| No. of attributes after FS | 8 | 55 | 30 | 20 | 17 | 53 | 30 | 21 | 20 | 71 | 30 | 29 | 32 | 32 | 30 | 31 |
| Classification algorithm | ||||||||||||||||
| J48 | 63.1783 | 64.7287 | 63.5659 | 67.4419 | 61.25 | 59.375 | 57.5 | 64.375 | 73.1618 | 66.1765 | 74.6324 | 64.7059 | 94 | 92.3333 | 70 | 94 |
| BFTree | 71.7054 | 68.9922 | 62.0155 | 76.3566 | 58.125 | 56.25 | 65 | 65.625 | 83.0882 | 79.7794 | 85.6618 | 81.25 | 93.3333 | 91.6666 | 68 | 93.3333 |
| FT | 74.031 | 79.0698 | 72.8682 | 82.5581 | 64.375 | 78.125 | 65.625 | 88.75 | 97.4265 | 88.9706 | 93.75 | 88.2353 | 72.6667 | 71 | 69.3333 | 76 |
| LMT | 63.5659 | 92.4419 | 73.6434 | 97.6744 | 60.625 | 83.125 | 70 | 88.125 | 96.3235 | 92.4465 | 91.9118 | 87.8676 | 86.6667 | 85 | 66.6667 | 86.6667 |
| NBTree | 71.7054 | 63.1783 | 63.1783 | 70.155 | 51.25 | 59.375 | 67.5 | 66.25 | n/a | n/a | n/a | n/a | 67.3333 | 65.6666 | 66.6667 | 68 |
| RandomForest | 73.6434 | 70.5426 | 67.8295 | 72.8682 | 61.875 | 74.375 | 71.25 | 73.125 | 90.4412 | 74.2647 | 81.9853 | 81.9853 | 90.6667 | 89 | 70.6667 | 90.6667 |
| RandomTree | 64.7287 | 59.6899 | 55.814 | 70.155 | 45 | 66.25 | 57.5 | 67.5 | 69.1176 | 61.7647 | 69.4853 | 63.9706 | 85.3333 | 83.6666 | 84.6667 | 85.3333 |
| REPTree | 72.093 | 72.093 | 67.4419 | 73.6434 | 56.875 | 61.25 | 60 | 64.375 | 84.9265 | 84.1912 | 79.4118 | 83.4559 | 94.6667 | 93 | 66.6667 | 94.6667 |
| ConjunctiveRule | 55.814 | 55.814 | 48.8372 | 65.8915 | 54.375 | 55 | 55 | 56.25 | n/a | n/a | n/a | n/a | 66.6667 | 65 | 65.3333 | 66.6667 |
| DecisionTable | 70.155 | 70.155 | 53.876 | 68.6047 | 57.5 | 56.25 | 61.875 | 52.5 | 67.2794 | 58.8235 | 63.2353 | 59.5588 | 93.3333 | 91.6666 | 77.3333 | 93.3333 |
| FURIA | 71.7054 | 77.1318 | 63.9535 | 74.8062 | 62.5 | 68.75 | 64.375 | 53.125 | 80.5147 | 62.5 | 78.6765 | 86.3971 | 84 | 82.3333 | 70 | 84 |
| JRip | 72.8682 | 71.7054 | 67.8295 | 73.6434 | 66.25 | 54.375 | 58.125 | 55 | 74.2647 | 34.9265 | 72.0588 | 65.4412 | 98 | 96.3333 | 66.6667 | 98 |
| NNge | 68.9922 | 66.6667 | 55.814 | 63.5659 | 53.125 | 46.25 | 52.5 | 50.625 | 92.6471 | 91.1765 | 92.2794 | 81.25 | 87.3333 | 85.6666 | 69.3333 | 87.3333 |
| OneR | 50.3876 | 50.3876 | 54.2636 | 54.2636 | 54.375 | 54.375 | 54.375 | 61.25 | 55.1471 | 55.1471 | 49.2647 | 49.2647 | 93.3333 | 91.6666 | 54 | 93.3333 |
| PART | 64.3411 | 68.6047 | 62.4031 | 68.6047 | 58.75 | 56.875 | 58.125 | 70.625 | 73.5294 | 67.2794 | 80.5147 | 71.3235 | 94 | 92.3333 | 69.3333 | 94 |
| NaiveBayes | 70.9302 | 64.7287 | 67.4419 | 67.0543 | 64.375 | 68.75 | 58.75 | 58.75 | 95.2206 | 76.8382 | 86.0294 | 72.0588 | 78.6667 | 77 | 64 | 78.6667 |
| Bagging | 73.2558 | 75.1938 | 70.155 | 75.969 | 63.75 | 64.375 | 68.75 | 53.125 | 89.3382 | 85.6618 | 84.9265 | 86.3971 | 93.3333 | 91.6666 | 66.6667 | 94.6667 |
| LibSVM | 66.6667 | 87.5969 | 63.5659 | 89.9225 | n/a | n/a | n/a | n/a | n/a | n/a | n/a | n/a | 57.3333 | 55.6666 | 46.6667 | 57.3333 |
| MultilayerPerceptron | 68.6047 | 79.0698 | 72.4806 | 89.5349 | 63.75 | 70.625 | 61.875 | 66.875 | 91.9118 | 93.75 | 93.75 | 86.3971 | 100 | 98.3333 | 70 | 100 |
| SMO | 72.4806 | 77.907 | 73.6434 | 79.4574 | 76.875 | 75.625 | 61.875 | 67.5 | 93.0147 | 93.75 | 93.3824 | 80.5147 | 98 | 96.3333 | 76 | 99.3333 |
| Mean accuracy % | 68.04263 | 70.78489 | 64.03102 | 74.108535 | 59.73684 | 63.65132 | 61.57895 | 64.40789 | 82.78547 | 74.55568 | 80.64448 | 75.88668 | 86.43333 | 84.76663 | 67.90001 | 86.76667 |
| Time (s) | 0.78 | 2.867 | 3.56 | 31.275 | 1.03 | 3.328 | 1.439 | 441.476 | 1.91 | 3.815 | 3.26 | 3585 | 1.39 | 2.17 | 4.8 | 906 |