Active Selection of Training Examples for Meta-Learning
- 1 September 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 126-131
Abstract
Meta-learning has been used to relate the performance of algorithms and the features of the problems being tackled. The knowledge in meta-learning is acquired from a set of meta-examples which are generated from the empirical evaluation of the algorithms on problems in the past. In this work, active learning is used to reduce the number of meta-examples needed for meta-learning. The motivation is to select only the most relevant problems for meta-example generation, and consequently to reduce the number of empirical evaluations of the candidate algorithms. Experiments were performed in two different case studies, yielding promising results.Keywords
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