Table 2: Comparison between the average predictive accuracy obtained by the best model (column three) and the average predictive accuracy of the best extracted rulesets (column five). The last three columns indicate the average fidelity, the average number of generated rules, and the average number of antecedents per rule, respectively.

Dataset Model Avg. Acc. Model Avg. rules Acc. Diff. Fid. #rules #Ant.

Australian Credit Appr.DIMLP-B 86.7DIMLP-B 86.5 0.2 97.9 21.4 2.8
Breast Cancer QSVM-G 97.2 QSVM-G 96.7 0.5 98.7 11.6 2.9
Breast Cancer 2 BST-G1 97.5 BST-G1 96.4 1.1 97.6 31.6 3.6
Breast Canc. (prognostic)DIMLP-B 81.0DIMLP-B 79.0 2.0 94.8 12.6 2.8
Bupa Liver DisordersDIMLP-B 72.7 BST-M4 71.1 1.6 91.8 37.2 3.4
Chess (kr-versus-kp) BST-G3 99.6 BST-G3 99.8 99.7 37.0 4.3
Coronary Heart DiseaseDIMLP-A 94.6DIMLP-A 92.3 2.3 96.2 71.6 4.6
German Credit QSVM-P3 75.9 QSVM-P3 75.1 0.8 94.4 85.4 5.4
Glass (binary) BST-G4 88.2 BST-M3 85.4 2.8 95.8 16.7 3.2
Haberman BST-M1 75.0 BST-M1 75.0 0.0 100.0 3.0 1.3
Heart Disease DIMLP-B 85.8 DIMLP-B 84.3 1.5 95.2 20.6 3.2
ILPD (liver) BST-G3 70.8 DIMLP-A 70.8 0.0 96.8 25.2 2.8
Ionosphere QSVM-G 94.4 BST-M1 92.1 2.3 97.8 14.3 2.8
Istanbul Stock Exch. QSVM-L 77.7 QSVM-L 77.6 0.1 94.4 30.0 3.0
Labor QSVM-G 95.1 BST-R4 89.6 5.5 95.4 9.8 2.5
Musk1 DIMLP-A 94.1 BST-G4 87.2 6.9 91.7 78.1 4.5
Pima Indians QSVM-G 76.8 DIMLP-B 76.3 0.5 97.0 38.8 3.3
Promoters BST-G3 92.1 BST-M1 84.7 7.4 90.7 11.4 2.7
Saheart DIMLP-B 72.7 BST-M3 72.3 0.4 97.5 26.3 3.4
Sonar BST-G4 88.6 BST-M3 81.9 6.7 88.3 35.8 4.2
Spect Heart DIMLP-B 73.0 DIMLP-B 72.2 0.8 94.8 20.4 3.2
Splice junct. BST-M4 97.3 BST-M4 97.1 0.2 99.1 63.1 4.7
Svmguide BST-M4 97.3 BST-M4 97.2 0.1 99.7 44.5 3.1
Tictactoe BST-G4 99.9 BST-G4 100 99.9 28.6 4.0
Vertebral ColumnDIMLP-B 85.5DIMLP-B 84.0 1.5 96.1 16.7 2.7