Construction of an off-centered entropy for supervised learning
Résumé
In supervised learning, many measures are based on the concept of entropy. A major characteristic of the entropies is that they take their maximal value when the distribution of the modalities of the class variable is uniform. To deal with the case where the a priori frequencies of the class variable modalities are very imbalanced, we propose an off-centered entropy which takes its maximum value for a distribution fixed by the user. This distribution can be the a priori distribution of the class variable modalities or a distribution taking into account the costs of misclassification.
Domaines
Ordinateur et société [cs.CY]Origine | Fichiers produits par l'(les) auteur(s) |
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