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PKDD
2015
Springer

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

7 years 11 months ago
Algorithm Selection via Meta-learning and Sample-based Active Testing
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 sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.
Salisu Abdulrahman, Pavel Brazdil, Jan N. van Rijn
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PKDD
Authors Salisu Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
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