BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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
Support Vector Data Description
Machine Learning
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Hierarchical multi-label prediction of gene function
Bioinformatics
Paired Comparisons Method for Solving Multi-Label Learning Problem
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Label ranking by learning pairwise preferences
Artificial Intelligence
Multilabel classification via calibrated label ranking
Machine Learning
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Improved Multilabel Classification with Neural Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A Fast Multi-label Classification Algorithm Based on Double Label Support Vector Machine
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Constructing a Fast Algorithm for Multi-label Classification with Support Vector Data Description
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
Fast multi-label core vector machine
Pattern Recognition
Expert Systems with Applications: An International Journal
Random block coordinate descent method for multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
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Hybrid strategy, which generalizes a specific single-label algorithm while one or two data decomposition tricks are applied implicitly or explicitly, has become an effective and efficient tool to design and implement various multi-label classification algorithms. In this paper, we extend traditional binary support vector machine by introducing an approximate ranking loss as its empirical loss term to build a novel support vector machine for multi-label classification, resulting into a quadratic programming problem with different upper bounds of variables to characterize label correlation of individual instance. Further, our optimization problem can be solved via combining one-versus-rest data decomposition trick with modified binary support vector machine, which dramatically reduces computational cost. Experimental study on ten multi-label data sets illustrates that our method is a powerful candidate for multi-label classification, compared with four state-of-the-art multi-label classification approaches.