Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes

Authors:
Julián Luengo;Francisco Herrera
Affiliations:
Dept. of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Granada, Spain 18071;Dept. of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Granada, Spain 18071
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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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.