Zoulficar Younes

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A formalism is proposed for representing uncertain information on set-valued variables using the formalism of belief functions. A set-valued variable X on a domain Ω is a variable taking zero, one or several values in Ω. While defining mass functions on the frame 2 2 Ω is usually not feasible because of the double-exponential complexity involved, we propose(More)
In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. Common approaches to multi-label classification learn independent classifiers for each category, and perform ranking or thresholding schemes in order to obtain(More)
In multilabel classification, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly used approach for multilabel classification is where a binary classifier is learned independently for each possible class. However, multilabeled(More)
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