MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Thresholding for making classifiers cost-sensitive
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Active cost-sensitive learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semi-supervised self-training for sentence subjectivity classification
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Semi-Supervised Learning
Instance selection in semi-supervised learning
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
SETRED: self-training with editing
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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In some real-world applications, it is time-consuming or expensive to collect much labeled data, while unlabeled data is easier to obtain. Many semi-supervised learning methods have been proposed to deal with this problem by utilizing the unlabeled data. On the other hand, on some datasets, misclassifying different classes causes different costs, which challenges the common assumption in classification that classes have the same misclassification cost. For example, misclassifying a fraud as a legitimate transaction could be more serious than misclassifying a legitimate transaction as fraudulent. In this paper, we propose a cost-sensitive self-training method (CS-ST) to improve the performance of Naive Bayes when labeled instances are scarce and different misclassification errors are associated with different costs. CS-ST incorporates the misclassification costs into the learning process of self-training, and approximately estimates the misclassification error to help select unlabeled instances. Experiments on 13 UCI datasets and three text datasets show that, in terms of the total misclassification cost and the number of correctly classified instances with higher costs, CS-ST has better performance than the self-training method and the base classifier learned from the original labeled data only.