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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… (More)
- Weiwei Cheng, Eyke Hüllermeier
- Machine Learning
- 2009
Multilabel classification 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 conventional… (More)
- Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, Klaus Brinker
- Artif. Intell.
- 2008
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent literature, the… (More)
- Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
- Machine Learning
- 2012
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, in one way or the other, dependencies between the class labels. Comparing to simple binary relevance… (More)
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement… (More)
The F-measure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and… (More)
We consider the problem of reliably selecting an optimal subset of fixed size from a given set of choice alternatives, based on noisy information about the quality of these alternatives. Problems of… (More)
This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the PL model to fit… (More)
- Weiwei Cheng, Michaël Rademaker, Bernard De Baets, Eyke Hüllermeier
- ECML/PKDD
- 2010
The prediction of structured outputs in general and rankings in particular has attracted considerable attention in machine learning in recent years, and different types of ranking problems have… (More)
- Johannes Fürnkranz, Eyke Hüllermeier, Weiwei Cheng, Sang-Hyeun Park
- Machine Learning
- 2012
This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a preference-based… (More)