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Multilabel classication is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for standard classication, instance-based learning algorithms relying on the nearest neighbor estimation
principle can be used quite successfully in this context. In this paper, we propose a new instance-based approach to multilabel classification, which is based on calibrated label ranking, a recently proposed
framework that unies multilabel classication and label ranking. Within
this framework, instance-based prediction is realized is the form of MAP
estimation, assuming a statistical distribution called the Mallows model.