anytime algorithms for learning anytime classifiers saher ... - Technion
anytime algorithms for learning anytime classifiers saher ... - Technion
anytime algorithms for learning anytime classifiers saher ... - Technion
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<strong>Technion</strong> - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008<br />
Table 4.8: Average cost of classification as a percentage of the standard cost of<br />
classification <strong>for</strong> mc = 10000. The first 25 rows list <strong>for</strong> each dataset the average<br />
cost over the different 4 cost-assignments, while the last 5 rows give the results <strong>for</strong><br />
the datasets with costs from (Turney, 1995).<br />
Dataset C4.5 LSID3 IDX CSID3 EG2 DTMC ICET ACT<br />
Breast-cancer 85.8 98.9 90.0 91.8 90.8 84.4 81.7 84.0<br />
Bupa 93.2 91.2 92.1 91.8 91.2 81.9 88.1 86.2<br />
Car 25.0 28.4 51.1 28.9 46.7 93.0 24.7 24.2<br />
Flare 77.7 77.7 75.1 77.3 75.1 77.9 78.1 72.8<br />
Glass 53.8 47.3 77.5 66.0 70.2 50.6 51.2 48.3<br />
Heart 52.0 61.9 65.2 57.1 63.4 52.8 53.2 59.8<br />
Hepatitis 86.8 80.1 87.5 88.8 85.9 72.9 64.8 70.7<br />
Iris 9.9 9.7 16.9 12.8 15.4 9.1 8.6 8.1<br />
KRK 51.3 62.0 67.9 63.7 66.5 49.0 46.4 45.8<br />
Multi-ANDOR 49.3 4.4 36.2 34.4 37.5 75.4 10.4 2.0<br />
Monks1 3.0 1.5 3.0 2.9 3.1 50.9 4.6 1.5<br />
Monks2 99.6 99.2 100.6 101.8 100.6 93.1 93.1 86.0<br />
Monks3 5.3 5.1 5.6 5.1 5.2 10.0 5.1 5.1<br />
Multiplexer 73.1 9.0 79.2 75.9 79.4 79.2 31.6 4.7<br />
MultiXOR 66.7 3.6 66.7 67.1 68.1 79.8 52.1 4.0<br />
Nursery 7.5 7.8 9.1 8.1 8.8 15.5 5.1 6.7<br />
Pima 77.8 86.5 88.2 77.4 89.5 75.8 72.9 78.3<br />
Tae 88.9 81.6 81.8 81.0 83.2 66.8 83.4 78.2<br />
Tic-tac-toe 48.2 46.0 55.9 46.7 55.5 58.2 41.5 45.5<br />
Titanic 65.9 66.8 65.0 65.2 65.0 67.1 65.3 65.3<br />
Thyroid 17.2 32.8 18.6 17.0 16.9 17.0 16.7 24.2<br />
Voting 8.4 10.2 16.2 9.4 14.6 7.5 8.0 8.9<br />
Wine 9.8 13.1 47.6 23.1 40.3 18.9 13.6 14.2<br />
XOR3d 92.4 41.7 98.2 87.8 97.3 101.7 63.1 45.1<br />
XOR5 94.5 7.0 111.0 107.6 111.7 93.3 92.9 33.6<br />
Bupa 93.5 92.2 93.5 88.8 93.5 81.1 85.6 90.4<br />
Heart 54.1 64.2 59.2 59.4 60.6 62.0 55.9 61.1<br />
Hepatitis 104.7 97.8 79.3 85.7 85.7 86.2 91.8 86.4<br />
Pima 78.1 87.3 105.9 92.4 105.9 77.0 72.4 81.0<br />
Thyroid 14.6 36.0 17.4 14.9 13.6 13.7 14.0 17.9<br />
represent the difficult domains, such as XOR, which ICET could not learn but<br />
ACT could.<br />
4.6.4 Comparing the Accuracy of the Learned Models<br />
When misclassification costs are low, an optimal algorithm would produce a very<br />
shallow tree. When misclassification costs are dominant, an optimal algorithm<br />
91
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