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
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Bayesian Models for Early Warning of Bank Failures
Management Science
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Journal of Management Information Systems - Special section: Data mining
Performance evaluation of neural network decision models
Journal of Management Information Systems - Special section: Strategic and competitive information systems
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
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication
Journal of Management Information Systems
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
Profiling Web Usage in the Workplace: A Behavior-Based Artificial Intelligence Approach
Journal of Management Information Systems
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Tuning expert systems for cost-sensitive decisions
Advances in Artificial Intelligence
Multiple costs based decision making with back-propagation 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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Real-world predictive data mining (classification or regression) problems are often cost sensitive, meaning that different types of prediction errors are not equally costly. While cost-sensitive learning methods for classification problems have been extensively studied recently, cost-sensitive regression has not been adequately addressed in the data mining literature yet. In this paper, we first advocate the use of average misprediction cost as a measure for assessing the performance of a cost-sensitive regression model. We then propose an efficient algorithm for tuning a regression model to further reduce its average misprediction cost. In contrast with previous statistical methods, which are tailored to particular cost functions, this algorithm can deal with any convex cost functions without modifying the underlying regression methods. We have evaluated the algorithm in bank loan charge-off forecasting, where underforecasting is considered much more costly than overforecasting. Our results show that the proposed algorithm significantly reduces the average misprediction costs of models learned with various base regression methods, such as linear regression, model tree, and neural network. The amount of cost reduction increases as the difference between the unit costs of the two types of errors (overprediction and underprediction) increases.