Name
Abstract | ||
|---|---|---|
To evaluate and compare the performance of different rule-ranking algorithms for rule-based classifiers on biomedical datasets.Empirical evaluation of five rule ranking algorithms on two biomedical datasets, with performance evaluation based on ROC analysis and 5 × 2 cross-validation.On a lung cancer dataset, the area under the ROC curve (AUC) of, on average, 14267.1 rules was 0.862. Multi-rule ranking found 13.3 rules with an AUC of 0.852. Four single-rule ranking algorithms, using the same number of rules, achieved average AUC values of 0.830, 0.823, 0.823, and 0.822, respectively. On a prostate cancer dataset, an average of 339265.3 rules had an AUC of 0.934, while 9.4 rules obtained from multi-rule and single-rule rankings had average AUCs of 0.932, 0.926, 0.925, 0.902 and 0.902, respectively.Multi-variate rule ranking performs better than the single-rule ranking algorithms. Both single-rule and multi-rule methods are able to substantially reduce the number of rules while keeping classification performance at a level comparable to the full rule set. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.artmed.2010.03.005 | Artificial Intelligence In Medicine |
Keywords | Field | DocType |
single-rule ranking,lung cancer,rule ranking algorithm,single-rule ranking algorithm,prostate cancer,full rule set,multi-rule ranking,average aucs,rule evaluation metrics,multi-variate rule ranking,rule ranking,classification performance,average auc value,biomedical datasets,rule based | Learning to rank,Data mining,Ranking SVM,Ranking,Computer science,Heuristics,Artificial intelligence,Area under the roc curve,Machine learning | Journal |
Volume | Issue | ISSN |
50 | 3 | 1873-2860 |
Citations | PageRank | References |
1 | 0.35 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephan Dreiseitl | 1 | 338 | 34.80 |
| Melanie Osl | 2 | 71 | 6.83 |
| Christian Baumgartner | 3 | 100 | 14.03 |
| Staal Vinterbo | 4 | 361 | 32.66 |