BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Hierarchical multi-label prediction of gene function
Bioinformatics
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Multilabel classification via calibrated label ranking
Machine Learning
Decision trees for hierarchical multi-label classification
Machine Learning
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Learning to tag from open vocabulary labels
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Max-margin embedding for multi-label learning
Pattern Recognition Letters
Sentiment and topic analysis on social media: a multi-task multi-label classification approach
Proceedings of the 5th Annual ACM Web Science Conference
| Hi-index | 0.00 |
We consider a hypercube view to perceive the label space of multilabel classification problems geometrically. The view allows us not only to unify many existing multilabel classification approaches but also design a novel algorithm, principal label space transformation (PLST), that captures key correlations between labels before learning. The simple and efficient PLST relies on only singular value decomposition as the key step. We derive the theoretical guarantee of PLST and evaluate its empirical performance using real-world data sets. Experimental results demonstrate that PLST is faster than the traditional binary relevance approach and is superior to the modern compressive sensing approach in terms of both accuracy and efficiency.