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dc.contributor.authorGuvenir, H. A.en_US
dc.contributor.authorKurtcephe, M.en_US
dc.date.accessioned2016-02-08T09:35:53Z
dc.date.available2016-02-08T09:35:53Z
dc.date.issued2013en_US
dc.identifier.issn1041-4347
dc.identifier.urihttp://hdl.handle.net/11693/20817
dc.description.abstractIn recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC) is a widely accepted performance measure for evaluating the quality of ranking, the algorithm aims to maximize the AUC value directly. For a single categorical feature, we show the necessary and sufficient condition that any ranking function must satisfy to achieve the maximum AUC. We also sketch a method to discretize a continuous feature in a way to reach the maximum AUC as well. RIMARC uses a heuristic to extend this maximization to all features of a data set. The ranking function learned by the RIMARC algorithm is in a human-readable form; therefore, it provides valuable information to domain experts for decision making. Performance of RIMARC is evaluated on many real-life data sets by using different state-of-the-art algorithms. Evaluations of the AUC metric show that RIMARC achieves significantly better performance compared to other similar methods. © 1989-2012 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Knowledge & Data Engineeringen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TKDE.2012.214en_US
dc.subjectData miningen_US
dc.subjectDecision supporten_US
dc.subjectMachine learningen_US
dc.subjectRankingen_US
dc.subjectArea under roc curve (AUC)en_US
dc.subjectCategorical featuresen_US
dc.subjectMachine learning literatureen_US
dc.subjectReal - valued functionsen_US
dc.subjectState - of - the - art algorithmsen_US
dc.subjectDecision support systemsen_US
dc.subjectInformation retrievalen_US
dc.subjectLearning systemsen_US
dc.subjectAlgorithmsen_US
dc.titleRanking instances by maximizing the area under ROC curveen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage2356en_US
dc.citation.epage2366en_US
dc.citation.volumeNumber25en_US
dc.citation.issueNumber10en_US
dc.identifier.doi10.1109/TKDE.2012.214en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US


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