Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Perceptron Algorithm with Uneven Margins
ICML '02 Proceedings of the Nineteenth International 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
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Multi-Classification by Using Tri-Class SVM
Neural Processing Letters
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Approximate maximum margin algorithms with rules controlled by the number of mistakes
Proceedings of the 24th international conference on Machine learning
Multilabel classification via calibrated label ranking
Machine Learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
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
A new perspective on an old perceptron algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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
Efficient prediction algorithms for binary decomposition techniques
Data Mining and Knowledge Discovery
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
Hybrid decision tree architecture utilizing local SVMs for multi-label classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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The pairwise approach to multilabel classification reduces the problem to learning and aggregating preference predictions among the possible labels. A key problem is the need to query a quadratic number of preferences for making a prediction. To solve this problem, we extend the recently proposed QWeighted algorithm for efficient pairwise multiclass voting to the multilabel setting, and evaluate the adapted algorithm on several real-world datasets. We achieve an average-case reduction of classifier evaluations from n^2 to n+dnlogn, where n is the total number of possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets.