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
      • Computer Science Working Paper Series
      • 1999 Working Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1999 Working Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Feature selection for discrete and numeric class machine learning

      Hall, Mark A.
      Thumbnail
      Files
      uow-cs-wp-1999-04.pdf
      762.8Kb
      Find in your library  
      Citation
      Export citation
      Hall, M.A. (1999). Feature selection for discrete and numeric class machine learning. (Working paper 99/04). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1033
      Abstract
      Algorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while filters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems.

      This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. Experiments using the new method as a preprocessing step for naïve Bayes, instance-based learning, decision trees, locally weighted regression, and model trees show it to be an effective feature selector - it reduces the data in dimensionality by more than sixty percent in most cases without negatively affecting accuracy. Also, decision and model trees built from the pre-processed data are often significantly smaller.
      Date
      1999-04
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      99/04
      Publisher
      Computer Science, University of Waikato
      Collections
      • 1999 Working Papers [16]
      Show full item record  

      Usage

      Downloads, last 12 months
      102
       
       

      Usage Statistics

      For this itemFor all of Research Commons

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