• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Ranking instances by maximizing the area under ROC curve

      Thumbnail
      View / Download
      2.7 Mb
      Author
      Guvenir, H. A.
      Kurtcephe, M.
      Date
      2013
      Source Title
      IEEE Transactions on Knowledge & Data Engineering
      Print ISSN
      1041-4347
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      25
      Issue
      10
      Pages
      2356 - 2366
      Language
      English
      Type
      Article
      Item Usage Stats
      136
      views
      171
      downloads
      Abstract
      In 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.
      Keywords
      Data mining
      Decision support
      Machine learning
      Ranking
      Area under roc curve (AUC)
      Categorical features
      Machine learning literature
      Real - valued functions
      State - of - the - art algorithms
      Decision support systems
      Information retrieval
      Learning systems
      Algorithms
      Permalink
      http://hdl.handle.net/11693/20817
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TKDE.2012.214
      Collections
      • Department of Computer Engineering 1368
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy