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Combining Uncertainty Sampling methods for supporting the generation of meta-examples
- Source :
-
Information Sciences . Aug2012, Vol. 196, p1-14. 14p. - Publication Year :
- 2012
-
Abstract
- Abstract: Meta-Learning aims to automatically acquire knowledge relating features of learning problems to the performance of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) stores features of a learning problem plus the performance obtained by a set of algorithms when evaluated on the problem. Based on a set of meta-examples, a Meta-Learner will be used to predict algorithm performance for new problems. The generation of a good set of meta-examples can be a costly process, since for each problem it is necessary to perform an empirical evaluation of the algorithms. In a previous work, we proposed the Active Meta-Learning, in which Active Learning was used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation. In the current work, we extend our previous research by combining different Uncertainty Sampling methods for Active Meta-Learning, considering that each individual method will provide useful information to select relevant problems. We also investigated the use of Outlier Detection to remove a priori those problems considered as outliers, aiming to improve the performance of the sampling methods. In our experiments, we observed a gain in Meta-Learning performance when the proposed combining method was compared to the individual active methods being combined and also when outliers were removed from the set of problems available for meta-example generation. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 196
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 73832192
- Full Text :
- https://doi.org/10.1016/j.ins.2012.02.003