Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on 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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
SMOTE: synthetic minority over-sampling technique
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
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 35.68 |
Most pattern recognition systems attempt to maximize the rate of correct recognition, regardless of the varying and often subjective significance of individual patterns. There are situations in which such uniform treatment of the recognition results may not be appropriate or sufficient, giving rise to the consideration of the non-uniform error cost issue and, in the context of machine learning, the cost-sensitive learning. Although a number of proposals to address the issue have been considered, the problem of system design or training that achieves minimum expected error cost remains an open one. This paper introduces a decision-theoretic framework based on the Bayes decision theory for applications with non-uniform error criteria. It addresses the two fundamental aspects in Bayes' decision theory, the optimal decision policy and the acquisition of system knowledge (i.e., training) for implementing the decision policy. The framework includes, among other components of the recognizer, a minimum risk decision rule, a smooth system objective function that serves as a surrogate for optimization involving non-uniform error costs, and a parameter optimization procedure to obtain the recognizer's parameters. To demonstrate and confirm the effectiveness of the proposed framework, Gaussian mixture classifiers are designed and implemented in experiments on multi-class datasets generated from Monte Carlo simulations as well as various prevalent machine learning datasets.