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
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
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
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
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Expert Systems with Applications: An International Journal
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
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
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
Random block coordinate descent method for multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Existing multi-label support vector machine (Rank-SVM) has an extremely high computational complexity and lacks an intrinsic zero point to determine relevant labels. In this paper, we propose a novel support vector machine for multi-label classification through both simplifying Rank-SVM and adding a zero label, resulting into a quadratic programming problem in which each class has an independent equality constraint. When Frank-Wolfe method is used to solve our quadratic programming problem iteratively, our entire linear programming problem of each step is divided into a series of sub-problems, which dramatically reduces computational cost. It is illustrated that for famous Yeast data set our training procedure runs about 12 times faster than Rank-SVM does under C++ environment. Experiments from five benchmark data sets show that our method is a powerful candidate for multi-label classification, compared with five state-of-the-art multi-label classification techniques.