Applied Computational Intelligence and Soft Computing / 2018 / Article / Tab 10 / Research Article
A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs 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 86.1 22.7 3.7 82.7 5.1 30.0 3.9 Breast Cancer 96.5 96.2 96.4 12.5 2.7 25.2 3.6 13.3 2.8 Breast Cancer 2 95.6 95.8 95.9 20.8 3.1 27.2 3.3 19.9 3.1 Breast Canc. (prognostic) 79.0 77.7 77.7 12.6 2.8 24.6 3.5 15.0 2.8 Bupa Liver Disorders 70.9 67.2 67.8 36.9 3.3 31.8 3.2 37.7 3.5 Chess (kr-versus-kp) 99.5 99.7 99.4 32.5 4.0 36.3 4.2 35.7 4.2 Coronary Heart Disease 91.6 92.3 90.9 44.8 4.0 71.6 4.6 53.1 4.2 German Credit 73.6 72.6 73.9 56.7 4.3 93.9 5.1 73.8 5.0 Glass (binary) 77.8 81.1 79.5 13.7 2.7 19.9 3.2 14.5 2.8 Haberman 74.3 73.3 72.8 7.8 1.9 2.3 0.6 6.7 1.8 Heart Disease 84.3 80.5 81.8 20.6 3.2 39.3 4.2 23.5 3.4 ILPD (liver) 70.7 70.8 68.8 23.9 3.1 25.2 2.8 31.1 3.2 Ionosphere 92.1 90.6 92.1 19.3 2.9 29.5 3.2 20.5 3.1 Istanbul Stock Exch. 77.2 75.1 76.0 21.4 2.8 23.4 2.9 26.0 3.0 Labor 84.3 87.4 86.1 7.3 2.2 9.2 2.6 7.2 2.2 Musk1 85.8 86.0 86.2 57.4 4.4 90.1 4.3 60.1 4.3 Pima Indians 76.3 74.2 75.5 38.8 3.3 47.0 3.6 42.3 3.6 Promoters 83.0 81.1 81.3 11.8 2.7 20.4 3.4 12.4 2.8 Saheart 71.9 68.6 70.9 29.2 3.3 27.5 3.3 29.2 3.4 Sonar 79.0 78.4 77.9 24.1 3.2 40.2 3.8 24.5 3.2 Spect heart 72.2 67.9 69.2 20.4 3.2 26.8 3.6 24.4 3.3 Splice junct. 95.1 95.3 94.6 108.3 5.5 172.6 6.4 124.6 7.7 Svmguide 96.8 96.9 96.8 38.0 2.9 69.8 3.2 38.7 2.9 Tictactoe 98.4 98.7 98.5 27.3 3.7 32.8 3.8 31.7 3.9 Vertebral Column 84.0 82.7 83.4 16.7 2.7 27.8 3.2 17.3 2.8