Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
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
Principles of data mining
Computer-Aided Multivariate Analysis
Computer-Aided Multivariate Analysis
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth 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
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Bayesian Models for Early Warning of Bank Failures
Management Science
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
Intelligent Data Analysis
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Journal of Management Information 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
Making words work: Using financial text as a predictor of financial events
Decision Support Systems
Using data mining techniques to predict hospitalization of hemodialysis patients
Decision Support Systems
Pairwise issue modeling for negotiation counteroffer prediction using neural networks
Decision Support Systems
Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment
Journal of Management Information Systems
Pattern Recognition
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In many real-world regression and forecasting problems, over-prediction and under-prediction errors have different consequences and incur asymmetric costs. Such problems entail the use of cost-sensitive learning, which attempts to minimize the expected misprediction cost, rather than minimize a simple measure such as mean squared error. A method has been proposed recently for tuning a regular regression model post hoc so as to minimize the average misprediction cost under an asymmetric cost structure. In this paper, we build upon that method and propose an extended tuning method for cost-sensitive regression. The previous method becomes a special case of the method we propose. We apply the proposed method to loan charge-off forecasting, a cost-sensitive regression problem that has had a bearing on bank failures over the last few years. Empirical evaluation in the loan charge-off forecasting domain demonstrates that the method we have proposed can further lower the misprediction cost significantly.