Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models

  • Authors:

  • H. Altun;A. Bilgil;B. C. Fidan

  • Affiliations:

  • Department of Electrical and Electronics Engineering, Nigde University, Nigde, Turkey;Department of Civil Engineering, Nigde University, Nigde, Turkey;Faculty of Electrical and Electronics Engineering, Department of Electrical Engineering, Yıldız Technical University, 34349 Beşiktaş/İstanbul, Turkey

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2007

Quantified Score

Hi-index 12.06

Visualization

Abstract

Successful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values.