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.
This work was supported by the European Commision, grant number IST-99-11764, as part of its framework V IST programme.
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Cawley, G.C., Dorling, S.R., Foxall, R.J., Mandic, D.P. (2001). Estimating the Costs Associated with Worthwhile Predictions of Poor Air Quality. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_121
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_121
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