Abstract
In this work we want to analyse the behaviour of two classic Artificial Neural Network models respect to a data complexity measures. In particular, we consider a Radial Basis Function Network and a Multi-Layer Perceptron. We examine the metrics of data complexity known as Measures of Separability of Classes over a wide range of data sets built from real data, and try to extract behaviour patterns from the results. We obtain rules that describe both good or bad behaviours of the Artificial Neural Networks mentioned.
With the obtained rules, we try to predict the behaviour of the methods from the data set complexity metrics prior to its application, and therefore establish their domains of competence.
This work has been supported by the Spanish Ministry of Science and Technology under Project TIN2008-06681-C06-01. J. Luengo holds a FPU scholarship from Spanish Ministry of Innovation and Science.
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Luengo, J., Herrera, F. (2009). Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_11
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DOI: https://doi.org/10.1007/978-3-642-02478-8_11
Publisher Name: Springer, Berlin, Heidelberg
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