Evaluation of neural network performance and generalisation using thresholding functions

S.G. Pierce, K. Worden, G. Manson

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The application of a simple thresholding technique to help assess the satisfactory performance of classification networks formed from Multi-Layer Perceptron (MLP) artificial neural networks (ANNs) is discussed. Both conventional Maximum Likelihood and Bayesian Evidence based training paradigms were
implemented. Firstly a simulated data set drawn from a two-dimensional Gaussian distribution was investigated to illustrate the physical significance of the threshold plots compared to the classifier output probability contours. Secondly a real world application data set comprising of low-frequency vibration measurements on an aircraft wing (a GNAT trainer) is considered. It is demonstrated that simple threshold based plots applied to classifier network outputs may provide a simple yet powerful technique to aid in the rejection of poorly regularised network structures.
Original languageEnglish
Pages (from-to)109-124
Number of pages16
JournalNeural Computing and Applications
Volume16
Issue number2
Early online date31 May 2006
DOIs
Publication statusPublished - 2007

Keywords

  • neural network training
  • thresholding
  • classification networks

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