Joint feature selection and classification for positive unlabelled multi-label data using weighted penalized empirical risk minimization - Teisseyre, Paweł (Zielona Góra: Uniwersytet Zielonogórski)
We consider the positive-unlabelled multi-label scenario in which multiple target variables are not observed directly. Instead, we observe surrogate variables indicating whether or not the target variables are labelled. The presence of a label means that the corresponding variable is positive. The absence of the label means that the variable can be either positive or negative.
Keywords: positive and unlabelled data; multi-label classification; feature selection; empirical risk minimization