An evaluation of heuristics for rule ranking

Authors:
Stephan Dreiseitl;Melanie Osl;Christian Baumgartner;Staal Vinterbo
Affiliations:
Department of Software Engineering, Upper Austria University of Applied Sciences at Hagenberg, Softwarepark 11, A-4232 Hagenberg, Austria;Institute of Electrical, Electronic and Bioengineering, University of Health Sciences, Medical Informatics and Technology, A-6060 Hall in Tyrol, Austria;Institute of Electrical, Electronic and Bioengineering, University of Health Sciences, Medical Informatics and Technology, A-6060 Hall in Tyrol, Austria;Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, United States
Venue:
Artificial Intelligence in Medicine
Year:
2010

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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.