Some comments of Wolfe's `away step'
Mathematical Programming: Series A and B
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
A Comparison of Ranking Methods for Classification Algorithm Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
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
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
Incorporating label dependency into the binary relevance framework for multi-label classification
Expert Systems with Applications: An International Journal
FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors
Expert Systems with Applications: An International Journal
Classifier chains for multi-label classification
Machine Learning
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
Fast multi-label core vector machine
Pattern Recognition
Multi-label classification based on analog reasoning
Expert Systems with Applications: An International Journal
Dependent binary relevance models for multi-label classification
Pattern Recognition
Multi-label classification by exploiting label correlations
Expert Systems with Applications: An International Journal
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Multi-label support vector machine with a zero label (Rank-SVMz) is an effective SVM-type technique for multi-label classification, which is formulated as a quadratic programming (QP) problem with several disjoint equality constraints and lots of box ones, and then is solved by Frank-Wolfe method (FWM) embedded one-versus-rest (OVR) decomposition trick. However, it is still highly desirable to speed up the training and testing procedures of Rank-SVMz for many real world applications. Due to the special disjoint equality constraints, all variables to be solved in Rank-SVMz are naturally divided into several blocks via OVR technique. Therefore we propose a random block coordinate descent method (RBCDM) for Rank-SVMz in this paper. At each iteration, an entire QP problem is divided into a series of small-scale QP sub-problems, and then each QP sub-problem with a single equality constraint and many box ones is solved by sequential minimization optimization (SMO) used in binary SVM. The theoretical analysis shows that RBCDM has a much lower time complexity than FWM for Rank-SVMz. Our experimental results on six benchmark data sets demonstrate that, on the average, RBCDM runs 11 times faster, produces 12% fewer support vectors, and achieves a better classification performance than FWM for Rank-SVMz. Therefore Rank-SVMz with RBCDM is a powerful candidate for multi-label classification.