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A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

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Abstract

This paper presents a fast and new deterministic model selection methodology for incremental radial basis function neural network (RBFNN) construction in time series prediction problems. The development of such special designed methodology is motivated by the problems that arise when using a K-fold cross-validation-based model selection methodology for this paradigm: its random nature and the subjective decision for a proper value of K, resulting in large bias for low values and high variance and computational cost for high values. Taking into account these drawbacks, the proposed model selection approach is a combined algorithm that takes advantage of two balanced and representative training and validation sets for their use in RBFNN initialization, optimization and network model evaluation. This way, the model prediction accuracy is improved, getting small variance and bias, reducing the computation time spent in selecting the model and avoiding random and computationally expensive model selection methodologies based on K-fold cross-validation procedures.

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Acknowledgments

This work was supported in part by the Spanish Project TIN2007-60587 and the FPU research grant AP2007-03009. The authors also want to thank all the anonymous reviewers for their suggestions and comments.

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Authors and Affiliations

  1. Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, 18071, Granada, Spain

    J. P. Florido, H. Pomares, I. Rojas, J. M. Urquiza & M. A. Lopez-Gordo

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  1. J. P. Florido
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  2. H. Pomares
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  3. I. Rojas
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  4. J. M. Urquiza
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  5. M. A. Lopez-Gordo
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Correspondence to J. P. Florido.

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Florido, J.P., Pomares, H., Rojas, I. et al. A deterministic model selection scheme for incremental RBFNN construction in time series forecasting. Neural Comput & Applic 21, 595–610 (2012). https://doi.org/10.1007/s00521-010-0466-5

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  • DOI: https://doi.org/10.1007/s00521-010-0466-5

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