Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Active learning of label ranking functions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Comparing and aggregating rankings with ties
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Unified Model for Multilabel Classification and Ranking
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Adapting decision DAGs for multipartite ranking
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Preferences in AI: An overview
Artificial Intelligence
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Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propose a sophisticated k-NN framework as an alternative to previous binary decomposition techniques. It exhibits the appealing property of transparency and is based on an aggregation model which allows one to incorporate a variety of pairwise loss functions on label rankings. In addition to these conceptual advantages, we empirically show that our case-based approach is competitive to state-of-the-art model-based learners with respect to accuracy while being computationally much more efficient. Moreover, our approach suggests a natural way to associate confidence scores with predictions, a property not being shared by previous methods.