Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Identifying and Handling Mislabelled Instances
Journal of Intelligent Information Systems
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Nearest neighbor editing aided by unlabeled data
Information Sciences: an International Journal
Learning from labeled and unlabeled data: an empirical study across techniques and domains
Journal of Artificial Intelligence Research
Semi-supervised learning based on nearest neighbor rule and cut edges
Knowledge-Based Systems
A stochastic approach to wilson's editing algorithm
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
SETRED: self-training with editing
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CoTrade: Confident Co-Training With Data Editing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Semi-supervised multi-label classification has been applied to many real-world applications such as image classification, document classification and so on. In semi-supervised learning, unlabeled samples are added to the training set for enhancing the classification performance, however, noises are introduced simultaneously. In order to reduce this negative effect, the nearest neighbor data editing technique is introduced to semi-supervised multi-label classification, and thus an algorithm named Multi-Label Self-Training with Editing (MLSTE) is proposed in this work. The proposed algorithm is able to solve the uncertainty problem in semi-supervised multi-label classification to some extent, by improving the performance of determining the label number and selecting confident samples during the course of semi-supervised learning. Extensive experimental results on several benchmark datasets have been carried out to verify the effectiveness of the proposed MLSTE algorithm.