Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Classifier chains for multi-label classification

      Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe
      DOI
       10.1007/978-3-642-04174-7_17
      Link
       www.springerlink.com
      Find in your library  
      Citation
      Export citation
      Read, J., Pfahringer, B., Holmes, G. & Frank, E. (2009). Classifier chains for multi-label classification. In Proceedings of European conference on Machine Learning and Knowledge Discovery in Databases 2009 (ECML PKDD 2009), Part II, LNAI 5782(pp. 254-269). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/3259
      Abstract
      The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
      Date
      2009
      Type
      Conference Contribution
      Publisher
      Springer
      Collections
      • Computing and Mathematical Sciences Papers [1455]
      Show full item record  

      Usage

       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement