Optimizing F-Measures by Cost-Sensitive Classification
Résumé
We present a theoretical analysis of F-measures for binary, multiclass and mul-tilabel classification. These performance measures are non-linear, but in many scenarios they are pseudo-linear functions of the per-class false negative/false positive rate. Based on this observation, we present a general reduction of F-measure maximization to cost-sensitive classification with unknown costs. We then propose an algorithm with provable guarantees to obtain an approximately optimal classifier for the F-measure by solving a series of cost-sensitive classification problems. The strength of our analysis is to be valid on any dataset and any class of classifiers, extending the existing theoretical results on F-measures, which are asymptotic in nature. We present numerical experiments to illustrate the relative importance of cost asymmetry and thresholding when learning linear classifiers on various F-measure optimization tasks.
Domaines
Apprentissage [cs.LG]
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5508-optimizing-f-measures-by-cost-sensitive-classification.pdf (1.18 Mo)
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