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Estimating the Costs Associated with Worthwhile Predictions of Poor Air Quality

  • Gavin C. Cawley
  • Stephen R. Dorling
  • Robert J. Foxall
  • Danilo P. Mandic
Conference paper

Abstract

In this study we investigate the effect of varying the ratio of false-positive and false-negative misclassification costs on the sensitivity and selectivity of binary predictions of exceedences of atmospheric pollutants. This allows us to determine a window of values far this ratio far which it is worthwhile making definite rather than probabilistic predictions. The support vector machine provides a suitable statistical pattern recognition method for this work.

Keywords

Support Vector Machine Training Pattern Misclassification Cost Support Vector Classification Radial Basis Func 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Gavin C. Cawley
    • 1
  • Stephen R. Dorling
    • 2
  • Robert J. Foxall
    • 1
  • Danilo P. Mandic
    • 1
  1. 1.School of Information SystemsUniversity of East AngliaNorwich, NorfolkUK
  2. 2.School of Environmental SciencesUniversity of East AngliaNorwich, NorfolkUK

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