Communications of the ACM
SIAM Journal on Discrete Mathematics
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Predicting Structured Data (Neural Information Processing)
Predicting Structured Data (Neural Information Processing)
Introduction to Information Retrieval
Introduction to Information Retrieval
Label ranking by learning pairwise preferences
Artificial Intelligence
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Binary Decomposition Methods for Multipartite Ranking
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Journal of Artificial Intelligence Research
Preference Learning
One tag to bind them all: measuring term abstractness in social metadata
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
Retrieval of semantic workflows with knowledge intensive similarity measures
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Similarity assessment and efficient retrieval of semantic workflows
Information Systems
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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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 already been studied. In this paper, we propose a generalization or, say, relaxation of the standard setting, allowing a model to make predictions in the form of partial instead of total orders. We interpret such kind of prediction as a ranking with partial abstention: If the model is not sufficiently certain regarding the relative order of two alternatives and, therefore, cannot reliably decide whether the former should precede the latter or the other way around, it may abstain from this decision and instead declare these alternatives as being incomparable. We propose a general approach to ranking with partial abstention as well as evaluation metrics for measuring the correctness and completeness of predictions. For two types of ranking problems, we show experimentally that this approach is able to achieve a reasonable trade-off between these two criteria.