Abstract
Meta-Learning predicts the performance of learning algorithms based on features of the learning problems. Meta-Learning acquires knowledge from a set of meta-examples, which store the experience obtained from applying the algorithms to problems in the past. A limitation of Meta-Learning is related to the generation of meta-examples. In order to construct a meta-example, it is necessary to empirically evaluate the algorithms on a given problem. Hence, the generation of a set of meta-examples may be costly depending on the context. In order to minimize this limitation, the use of Active Learning is proposed to reduce the number of required meta-examples. In this paper, we evaluate this proposal on a promising Meta-Learning approach, called Meta-Regression. Experiments were performed in a case study to predict the performance of learning algorithms for MLP networks. A significant performance gain was observed in the case study when Active Learning was used to support the generation of meta-examples.
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Prudêncio, R.B.C., Ludermir, T.B. (2009). Active Generation of Training Examples in Meta-Regression. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_4
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DOI: https://doi.org/10.1007/978-3-642-04274-4_4
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