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An evaluation of heuristics for rule ranking
An evaluation of heuristics for rule ranking
- Source :
- Artificial intelligence in medicine. 50(3)
- Publication Year :
- 2009
-
Abstract
- Objective: To evaluate and compare the performance of different rule-ranking algorithms for rule-based classifiers on biomedical datasets. Methodology: Empirical evaluation of five rule ranking algorithms on two biomedical datasets, with performance evaluation based on ROC analysis and 5x2 cross-validation. Results: 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. Conclusion: 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.
- Subjects :
- Male
Lung Neoplasms
Computer science
business.industry
Medicine (miscellaneous)
Prostatic Neoplasms
Breast Neoplasms
Machine learning
computer.software_genre
Ranking (information retrieval)
Set (abstract data type)
Artificial Intelligence
Ranking SVM
Area Under Curve
Humans
Learning to rank
Female
Data mining
Artificial intelligence
Heuristics
business
Area under the roc curve
computer
Algorithms
Subjects
Details
- ISSN :
- 18732860
- Volume :
- 50
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Artificial intelligence in medicine
- Accession number :
- edsair.doi.dedup.....01e2b3612ec760fde5d4761d2260bd85