Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors

Authors:
Silvio B. Guerra;Ricardo B. Prudêncio;Teresa B. Ludermir
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife (PE), Brazil CEP 50732-970;Center of Informatics, Federal University of Pernambuco, Recife (PE), Brazil CEP 50732-970;Center of Informatics, Federal University of Pernambuco, Recife (PE), Brazil CEP 50732-970
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to predict learning performance, supporting algorithm selection. Experiments were performed in a case study in which SVMs with different kernel functions were used to predict the performance of Multi-Layer Perceptron (MLP) networks. The SVMs obtained better results in the evaluated task, when compared to different algorithms that have been applied as meta-regressors in previous work.