Algorithm Selection via Meta-learning and Sample-based Active Testing

  title={Algorithm Selection via Meta-learning and Sample-based Active Testing},
  author={Salisu Abdulrahman and Pavel Brazdil and Jan N. van Rijn and Joaquin Vanschoren},
Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data… CONTINUE READING
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