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      Pairwise meta-rules for better meta-learning-based algorithm ranking

      Sun, Quan; Pfahringer, Bernhard
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      qs-mlj13.pdf
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      DOI
       10.1007/s10994-013-5387-y
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      Sun, Q., & Pfahringer, B. (2013). Pairwise meta-rules for better meta-learning-based algorithm ranking. Machine Learning, 93(1), 141-161.
      Permanent Research Commons link: https://hdl.handle.net/10289/7823
      Abstract
      In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset.
      Date
      2013-07
      Type
      Journal Article
      Publisher
      Springer-Verlag
      Rights
      © The Author(s) 2013. This is the authors' accepted version.
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      • Computing and Mathematical Sciences Papers [1455]
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