Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Journal of Machine Learning Research
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
Label ranking by learning pairwise preferences
Artificial Intelligence
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
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A sparse gaussian processes classification framework for fast tag suggestions
Proceedings of the 17th ACM conference on Information and knowledge management
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
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
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ceteris Paribus preference elicitation with predictive guarantees
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Mining multi-label concept-drifting data streams using ensemble classifiers
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
International Journal of Approximate Reasoning
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
Multilabel classification using error correction codes
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Voting based learning classifier system for multi-label classification
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Dual layer voting method for efficient multi-label classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Two stage architecture for multi-label learning
Pattern Recognition
Multi-instance multi-label learning
Artificial Intelligence
ECML'06 Proceedings of the 17th European conference on Machine Learning
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
Efficient multilabel classification algorithms for large-scale problems in the legal domain
Semantic Processing of Legal Texts
A model for multi-label classification and ranking of learning objects
Expert Systems with Applications: An International Journal
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
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Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. We present empirical results in the area of text categorization and gene analysis, underscoring the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.