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dc.contributor.authorOrtigosa-Hernández, J.
dc.contributor.authorInza, I.
dc.contributor.authorLozano, J.A.
dc.description.abstractSince many important real-world classification problems involve learning from unbalanced data, the challenging class-imbalance problem has lately received con- siderable attention in the community. Most of the methodological contributions proposed in the literature carry out a set of experiments over a battery of specific datasets. In these cases, in order to be able to draw meaningful conclusions from the experiments, authors often measure the class-imbalance extent of each tested dataset using imbalance-ratio, i.e. dividing the frequencies of the majority class by the minority class. In this paper, we argue that, although imbalance-ratio is an informative measure for binary problems, it is not adequate for the multi-class scenario due to the fact that, in that scenario, it groups problems with disparate class-imbalance extents under the same numerical value. Thus, in order to overcome this drawback, in this paper, we propose imbalance-degree as a novel and normalised measure which is capable of properly measuring the class-imbalance extent of a multi-class problem. Experimental results show that imbalance-degree is more adequate than imbalance- ratio since it is more sensitive in reflecting the hindrance produced by skewed multi- class distributions to the learning processes.en_US
dc.description.sponsorshipTIN2013-41272P, IT609-13, AP2008-00766en_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.titleMeasuring the Class-imbalance Extent of Multi-class Problemsen_US
dc.journal.titlePattern Recognition Letteren_US

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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España