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      • University of Waikato Research
      • Computing and Mathematical Sciences
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      Prediction of ordinal classes using regression trees

      Kramer, Stefan; Widmer, Gerhard; Pfahringer, Bernhard; de Groeve, Michael
      DOI
       10.1007/3-540-39963-1_45
      Link
       www.springerlink.com
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      Citation
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      Kramer, S., Widmer, G. Pfahringer, B. & DeGroeve, M. (2000). Prediction of ordinal classes using regression tree. In Z.W. Ras & S. Ohsuga(Eds), Proceedings of 12th International Symposium, ISMIS 2000 Charlotte, NC, USA, October 11–14, 2000. (pp. 665-674). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1467
      Abstract
      This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.
      Date
      2000
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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