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Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

Smith, C and Jin, Y (2014) Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction Neurocomputing, 143. pp. 302-311.

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Abstract

Ensembles have been shown to provide better generalization performance than single models. However, the creation, selection and combination of individual predictors is critical to the success of an ensemble, as each individual model needs to be both accurate and diverse. In this paper we present a hybrid multi-objective evolutionary algorithm that trains and optimizes the structure of recurrent neural networks for time series prediction. We then present methods of selecting individual prediction models from the Pareto set of solutions. The first method selects all individuals below a threshold in the Pareto front and the second one is based on the training error. Individuals near the knee point of the Pareto front are also selected and the final method selects individuals based on the diversity of the individual predictors. Results on two time series data sets, Mackey-Glass and Sunspot, show that the training algorithm is competitive with other algorithms and that the final two selection methods are better than selecting all individuals below a given threshold or based on the training error. © 2014 Elsevier B.V.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Smith, CUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 2 November 2014
Identification Number : 10.1016/j.neucom.2014.05.062
Additional Information : NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 143, November 2014, DOI 10.1016/j.neucom.2014.05.062.
Depositing User : Symplectic Elements
Date Deposited : 03 Feb 2015 12:27
Last Modified : 03 Feb 2015 12:27
URI: http://epubs.surrey.ac.uk/id/eprint/806702

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