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.

      Using weighted nearest neighbor to benefit from unlabeled data

      Driessens, Kurt; Reutemann, Peter; Pfahringer, Bernhard; Leschi, Claire
      DOI
       10.1007/11731139_10
      Link
       www.springerlink.com
      Find in your library  
      Citation
      Export citation
      Driessens, K., Reutemann, P., Pfahringer, B. & Leschi, C.(2006). Using weighted nearest neighbor to benefit from unlabeled data. In W.K. Ng, M. Kitsuregawa & J. Li(Eds.), Proceedings of 10th Pacific-Asia Conference, PAKDD, Singapore, April 9-12,2006(pp. 97-106). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1432
      Abstract
      The development of data-mining applications such as textclassification and molecular profiling has shown the need for machine learning algorithms that can benefit from both labeled and unlabeled data, where often the unlabeled examples greatly outnumber the labeled examples. In this paper we present a two-stage classifier that improves its predictive accuracy by making use of the available unlabeled data. It uses a weighted nearest neighbor classification algorithm using the combined example-sets as a knowledge base. The examples from the unlabeled set are “pre-labeled” by an initial classifier that is build using the limited available training data. By choosing appropriate weights for this pre-labeled data, the nearest neighbor classifier consistently improves on the original classifier.
      Date
      2006
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1454]
      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