Machine Learning
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
Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Direct Marketing Performance Modeling Using Genetic Algorithms
INFORMS Journal on Computing
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
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
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel(R) with XLMiner(TM) + Making Sense of Data Set
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
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Because of the unbalanced class and skewed profit distribution in customer purchase data, the unknown and variant costs of false negative errors are a common problem for predicting the high-value customers in marketing operations. Incorporating cost-sensitive learning into forecasting models can improve the return on investment under resource constraint. This study proposes a cost-sensitive learning algorithm via priority sampling that gives greater weight to the high-value customers. We apply the method to three data sets and compare its performance with that of competing solutions. The results suggest that priority sampling compares favorably with the alternative methods in augmenting profitability. The learning algorithm can be implemented in decision support systems to assist marketing operations and to strengthen the strategic competitiveness of organizations.