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.

      Fast perceptron decision tree learning from evolving data streams

      Bifet, Albert; Holmes, Geoffrey; Pfahringer, Bernhard; Frank, Eibe
      Thumbnail
      Files
      Perceptron.pdf
      401.6Kb
      DOI
       10.1007/978-3-642-13672-6_30
      Link
       www.springerlink.com
      Find in your library  
      Citation
      Export citation
      Bifet, A., Holmes, G., Pfahringer, B. & Frank, E. (2010). Fast perceptron decision tree learning from evolving data streams. In M.J. Zaki, J. X. Yu, B. Ravindran & V. Pudi (Eds.), Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II (pp. 299-310). Berlin, Germany: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/3983
      Abstract
      Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees—Hoeffding Trees with naive Bayes models at the leaf nodes—albeit with increased runtime compared to standard Hoeffding Trees. In this paper, we show that runtime can be reduced by replacing naive Bayes with perceptron classifiers, while maintaining highly competitive accuracy. We also show that accuracy can be increased even further by combining majority vote, naive Bayes, and perceptrons. We evaluate four perceptron-based learning strategies and compare them against appropriate baselines: simple perceptrons, Perceptron Hoeffding Trees, hybrid Naive Bayes Perceptron Trees, and bagged versions thereof. We implement a perceptron that uses the sigmoid activation function instead of the threshold activation function and optimizes the squared error, with one perceptron per class value. We test our methods by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.
      Date
      2010
      Type
      Conference Contribution
      Publisher
      Springer Berlin
      Rights
      This is an author’s accepted version of a paper published in Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II.
      Collections
      • Computing and Mathematical Sciences Papers [1455]
      Show full item record  

      Usage

      Downloads, last 12 months
      120
       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

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