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Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains

8 years 1 months ago
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label dependence into account. The goal of this paper is to elaborate on this postulate in a critical way. To this end, we formalize and analyze MLC within a probabilistic setting. Thus, it becomes possible to look at the problem from the point of view of risk minimization and Bayes optimal prediction. Moreover, inspired by our probabilistic setting, we propose a new method for MLC that generalizes and outperforms another approach, called classifier chains, that was recently introduced in the literature.
Krzysztof Dembczynski, Weiwei Cheng, Eyke Hül
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICML
Authors Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier
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