BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
Approximate and dynamic rank aggregation
Theoretical Computer Science - Special papers from: COCOON 2003
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
ECML'06 Proceedings of the 17th European conference on Machine Learning
Multilabel classification via calibrated label ranking
Machine Learning
Label Ranking in Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
A New Instance-Based Label Ranking Approach Using the Mallows Model
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
Multi-instance multi-label learning
Artificial Intelligence
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label classification based on analog reasoning
Expert Systems with Applications: An International Journal
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We present a case-based approach to multilabel ranking, a recent extension of the well-known problem of multilabel classification. Roughly speaking, a multilabel ranking refines a multilabel classification in the sense that, while the latter only splits a predefined label set into relevant and irrelevant labels, the former furthermore puts the labels within both parts of this bipartition in a total order. We introduce a conceptually novel framework, essentially viewing multilabel ranking as a special case of aggregating rankings which are supplemented with an additional virtual label and in which ties are permitted. Even though this framework is amenable to a variety of aggregation procedures, we focus on a particular technique which is computationally efficient and prove that it computes optimal aggregations with respect to the (generalized) Spearman rank correlation as an underlying loss (utility) function. Moreover, we propose an elegant generalization of this loss function and empirically show that it increases accuracy for the subtask of multilabel classification.