Measuring the complexity of regression problems

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2016
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Maciel, Aron Ifanger [UNIFESP]
Costa, Ivan G.
Lorena, Ana Carolina[UNIFESP]
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Many works have attempted to characterize the complexity of classification problems by measures extracted from their learning datasets. These indexes provide indicatives of the inherent difficulty in solving a given classification problem. Although regression problems are equally frequent, there is a lack of studies in Machine Learning dedicated to understanding their complexity. This paper proposes some measures aimed to characterize the complexity of regression problems. They are experimentally evaluated on a set of synthetic datasets with different complexities. The results show that various measures and their combinations are able to distinguish simple linear problems from more complex variants.
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2016 International Joint Conference On Neural Networks (IJCNN). New york, p. 1450-1457, 2016.
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