Assessing the performance of classifiers when classes arise from a continuum
Publication details
Journal : Computational Statistics & Data Analysis , vol. 46 , p. 689–705 , 2004
Publisher : Elsevier
International Standard Numbers
:
Printed
:
0167-9473
Electronic
:
1872-7352
Publication type : Academic article
Links
:
DOI
:
doi.org/10.1016/j.csda.2003.09...
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Kjetil Aune
Chief Librarian
kjetil.aune@nofima.no
Summary
The situation where classes arise by dividing the range of a continuous response variable into intervals is discussed. The focus is on assessing the performance of classifiers. Due to the underlying continuum, all misclassifications are not equally grave. The probability of misclassification (pmc) is not optimal in this situation. An alternative performance measure, the squared error rate (sqerr) is proposed. It is related to the mean squared error of regression, and penalises misclassifications according to their severity. Also, because of measurement errors in the response variable, there are misallocated class labels in data sets used for training and testing. Estimates of the pmc and the sqerr are developed for this situation. The estimates are tested and compared on a real data set and in a simulation.