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.

      Handling numeric attributes in Hoeffding trees

      Pfahringer, Bernhard; Holmes, Geoffrey; Kirkby, Richard Brendon
      DOI
       10.1007/978-3-540-68125-0_27
      Link
       www.springerlink.com
      Find in your library  
      Citation
      Export citation
      Pfahringer, B., Holmes, G. & Kirkby, R. (2008). Handling numeric attributes in Hoeffding trees. In Proceedings of 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 (pp. 296-307). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1730
      Abstract
      For conventional machine learning classification algorithms handling numeric attributes is relatively straightforward. Unsupervised and supervised solutions exist that either segment the data into pre-defined bins or sort the data and search for the best split points. Unfortunately, none of these solutions carry over particularly well to a data stream environment. Solutions for data streams have been proposed by several authors but as yet none have been compared empirically. In this paper we investigate a range of methods for multi-class tree-based classification where the handling of numeric attributes takes place as the tree is constructed. To this end, we extend an existing approximation approach, based on simple Gaussian approximation. We then compare this method with four approaches from the literature arriving at eight final algorithm configurations for testing. The solutions cover a range of options from perfectly accurate and memory intensive to highly approximate. All methods are tested using the Hoeffding tree classification algorithm. Surprisingly, the experimental comparison shows that the most approximate methods produce the most accurate trees by allowing for faster tree growth.
      Date
      2008
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      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