C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
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
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
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
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting cost-sensitive learning for reject option
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Multiple costs based decision making with back-propagation neural networks
Decision Support Systems
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In this paper we propose a very simple, yet general and effective method to make any cost-insensitive classifiers (that can produce probability estimates) cost-sensitive. The method, called Thresholding, selects a proper threshold from training instances according to the misclassification cost. Similar to other cost-sensitive meta-learning methods, Thresholding can convert any existing (and future) costinsensitive learning algorithms and techniques into costsensitive ones. However, by comparing with the existing cost sensitive meta-learning methods and the direct use of the theoretical threshold, Thresholding almost always produces the lowest misclassification cost. Experiments also show that Thresholding has the least sensitivity on the misclassification cost ratio. Thus, it is recommended to use when the difference on misclassification costs is large.