A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Boosting Trees for Cost-Sensitive Classifications
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Local sparsity control for naive Bayes with extreme misclassification costs
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the 1st international workshop on Utility-based data mining
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Proceedings of the 24th international conference on Machine learning
Cost-sensitive multi-class classification from probability estimates
Proceedings of the 25th international conference on Machine learning
Multi-class cost-sensitive boosting with p-norm loss functions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
Roulette Sampling for Cost-Sensitive Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Sensitive Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved naive bayes for extremely skewed misclassification costs
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Parameter inference of cost-sensitive boosting algorithms
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Many research work in cost-sensitive learning focused on binary class problems and assumed that the costs are precise. But real-world applications often have multiple classes and the costs cannot be obtained precisely. It is important to address these issues for cost-sensitive learning to be more useful for real-world applications. This paper gives a short introduction to cost-sensitive learning and then summaries some of our previous work related to the above two issues: (1) The analysis of why traditional Rescaling method fails to solve multi-class problems and our method Rescalenew . (2) The problem of learning with cost intervals and our CISVM method. (3) The problem of learning with cost distributions and our CODIS method.