Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results

@article{Brazdil2003RankingLA,
  title={Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results},
  author={Pavel Brazdil and Carlos Soares and Joaquim Pinto da Costa},
  journal={Machine Learning},
  year={2003},
  volume={50},
  pages={251-277}
}
We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a relatively small set of data characteristics, which was selected to represent properties that affect algorithm performance. The performance of the candidate algorithms on those datasets is used to generate a recommendation to the user in the form of a… CONTINUE READING
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