Table 10: Average predictive accuracy of extracted rulesets from DIMLP-Bs, DIMLP-As, and single DIMLPs (columns 2, 3, and 4). Average complexity of rulesets (last three columns).

Dataset DIMLP-B DIMLP-A DIMLP DIMLP-B DIMLP-A DIMLP

Australian Credit Appr.86.5 84.9 86.122.7 3.7 82.7 5.1 30.0 3.9
Breast Cancer96.5 96.2 96.412.5 2.7 25.2 3.6 13.3 2.8
Breast Cancer 2 95.6 95.895.9 20.8 3.1 27.2 3.319.9 3.1
Breast Canc. (prognostic)79.0 77.7 77.712.6 2.8 24.6 3.5 15.0 2.8
Bupa Liver Disorders70.9 67.2 67.8 36.9 3.331.8 3.2 37.7 3.5
Chess (kr-versus-kp) 99.599.7 99.432.5 4.0 36.3 4.2 35.7 4.2
Coronary Heart Disease 91.692.3 90.944.8 4.0 71.6 4.6 53.1 4.2
German Credit 73.6 72.673.956.7 4.3 93.9 5.1 73.8 5.0
Glass (binary) 77.881.1 79.513.7 2.7 19.9 3.2 14.5 2.8
Haberman74.3 73.3 72.8 7.8 1.92.3 0.6 6.7 1.8
Heart Disease84.3 80.5 81.820.6 3.2 39.3 4.2 23.5 3.4
ILPD (liver) 70.770.8 68.823.9 3.1 25.2 2.8 31.1 3.2
Ionosphere92.1 90.692.119.3 2.9 29.5 3.2 20.5 3.1
Istanbul Stock Exch.77.2 75.1 76.021.4 2.8 23.4 2.9 26.0 3.0
Labor 84.387.4 86.1 7.3 2.2 9.2 2.67.2 2.2
Musk1 85.8 86.086.257.4 4.4 90.1 4.3 60.1 4.3
Pima Indians76.3 74.2 75.538.8 3.3 47.0 3.6 42.3 3.6
Promoters83.0 81.1 81.311.8 2.7 20.4 3.4 12.4 2.8
Saheart71.9 68.6 70.929.2 3.3 27.5 3.3 29.2 3.4
Sonar79.0 78.4 77.924.1 3.2 40.2 3.8 24.5 3.2
Spect heart72.2 67.9 69.220.4 3.2 26.8 3.6 24.4 3.3
Splice junct. 95.195.3 94.6108.3 5.5 172.6 6.4 124.6 7.7
Svmguide 96.896.9 96.838.0 2.9 69.8 3.2 38.7 2.9
Tictactoe 98.498.7 98.527.3 3.7 32.8 3.8 31.7 3.9
Vertebral Column84.0 82.7 83.416.7 2.7 27.8 3.2 17.3 2.8