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.

      A diagnostic tool for tree based supervised classification learning algorithms

      Holmes, Geoffrey; Trigg, Leonard E.
      Thumbnail
      Files
      uow-cs-wp-1999-03.pdf
      414.6Kb
      Find in your library  
      Citation
      Export citation
      Holmes, G. & Trigg, L.(1999). A diagnostic tool for tree based supervised classification learning algorithms. (Working paper 99/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1032
      Abstract
      The process of developing applications of machine learning and data mining that employ supervised classification algorithms includes the important step of knowledge verification. Interpretable output is presented to a user so that they can verify that the knowledge contained in the output makes sense for the given application. As the development of an application is an iterative process it is quite likely that a user would wish to compare models constructed at various times or stages.

      One crucial stage where comparison of models is important is when the accuracy of a model is being estimated, typically using some form of cross-validation. This stage is used to establish an estimate of how well a model will perform on unseen data. This is vital information to present to a user, but it is also important to show the degree of variation between models obtained from the entire dataset and models obtained during cross-validation. In this way it can be verified that the cross-validation models are at least structurally aligned with the model garnered from the entire dataset.

      This paper presents a diagnostic tool for the comparison of tree-based supervised classification models. The method is adapted from work on approximate tree matching and applied to decision trees. The tool is described together with experimental results on standard datasets.
      Date
      1999-03
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      99/03
      Collections
      • 1999 Working Papers [16]
      Show full item record  

      Usage

      Downloads, last 12 months
      113
       
       

      Usage Statistics

      For this itemFor all of Research Commons

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