Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification

18 May 2018Marco FrascaNicolò Cesa-Bianchi

Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm must thus select which unlabeled examples to use as negative training points, possibly ending up with an unbalanced learning problem... (read more)

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