E. Miguelanez (UK) and A.M.S. Zalzala (UAE)
Artificial neural networks (ANNs), particle swarm opti mization (PSO), bias variance trade-off.
In the search of a system satisfying all the require ments of an automated classifier, evolutionary artificial neural networks have been successfully applied to a num ber of problems, in particular to problems where there is not a deep knowledge of the phenomena and other methods tend to fail. There are many neural models that efficiently solve either function approximation problems in general terms or some particular problems like classification, pat tern recognition, clustering and time-series prediction. This success is due to these models main characteristics, in par ticular those matching the essentials for an automated clas sifier: keeping bias and variance low. This paper presents a classifier system based on the benefits arising from the in teraction between evolutionary algorithms, such as particle swarm optimization, and artificial neural networks. And a bias variance decomposition of the predictive error shows that the success of the proposed approach lies in the ability of the learning algorithm to properly tune the bias/variance trade-off to reduce the prediction error. To measure the per formance, the porposed classifier will be tested on three different well known benchmark problems: the Fisher Iris data set, the Australian credit card assessment and the Pima diabetes data set.
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