On Data and Algorithms: Understanding Inductive Performance

@article{Kalousis2004OnDA,
  title={On Data and Algorithms: Understanding Inductive Performance},
  author={Alexandros Kalousis and Jo{\~a}o Gama and Melanie Hilario},
  journal={Machine Learning},
  year={2004},
  volume={54},
  pages={275-312}
}
In this paper we address two symmetrical issues, the discovery of similarities among classification algorithms, and among datasets. Both on the basis of error measures, which we use to define the error correlation between two algorithms, and determine the relative performance of a list of algorithms. We use the first to discover similarities between learners, and both of them to discover similarities between datasets. The latter sketch maps on the dataset space. Regions within each map exhibit… CONTINUE READING
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