Improving Prediction Interval Quality: A Genetic Algorithm-Based Method Applied to Neural Networks

  • Abbas Khosravi
  • Saeid Nahavandi
  • Doug Creighton
Conference paper

DOI: 10.1007/978-3-642-10684-2_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5864)
Cite this paper as:
Khosravi A., Nahavandi S., Creighton D. (2009) Improving Prediction Interval Quality: A Genetic Algorithm-Based Method Applied to Neural Networks. In: Leung C.S., Lee M., Chan J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg

Abstract

The delta technique has been proposed in literature for constructing prediction intervals for targets estimated by neural networks. Quality of constructed prediction intervals using this technique highly depends on neural network characteristics. Unfortunately, literature is void of information about how these dependences can be managed in order to optimize prediction intervals. This study attempts to optimize length and coverage probability of prediction intervals through modifying structure and parameters of the underlying neural networks. In an evolutionary optimization, genetic algorithm is applied for finding the optimal values of network size and training hyper-parameters. The applicability and efficiency of the proposed optimization technique is examined and demonstrated using a real case study. It is shown that application of the proposed optimization technique significantly improves quality of constructed prediction intervals in term of length and coverage probability.

Keywords

Neural network genetic algorithm prediction interval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Abbas Khosravi
    • 1
  • Saeid Nahavandi
    • 1
  • Doug Creighton
    • 1
  1. 1.Centre for Intelligent Systems Research (CISR)Deakin UniversityGeelongAustralia

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