We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. Traditional approaches in choice modelling are mainly based on learning a latent, real-valued utility function, thereby …
We consider the problem of (macro) F-measure maximization in the context of extreme multi-label classification (XMLC), i.e., multi-label classification with extremely large label spaces. We investigate several approaches based on recent results on …
In medical diagnosis, information about the health state of a patient can often be obtained through different tests, which may perhaps be combined into an overall decision rule. Practically, this leads to several important questions. For example, …