C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Software Engineering Economics
Software Engineering Economics
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Machine Learning
Concept-Learning in the Presence of Between-Class and Within-Class Imbalances
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Maximum profit mining and its application in software development
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Enterprise software venders often have to release software products before all reported defects are corrected, and a small number of these reported defects will be escalated by customers whose businesses are seriously impacted. Escalated defects must be quickly resolved at a high cost by the software vendors. The total costs can be even greater, including loss of reputation, satisfaction, loyalty, and repeat revenue. In this paper, we develop an Escalation Prediction (EP) system to mine historic defect report data and predict the escalation risk of current defect reports for maximum ROI (Return On Investment). More specifically, we first describe a simple and general framework to convert the maximum ROI problem to cost-sensitive learning. We then apply and compare several best-known cost-sensitive learning approaches for EP. The EP system has produced promising results, and has been deployed in the product group of an enterprise software vendor. Conclusions drawn from this study also provide guidelines for mining imbalanced datasets and cost-sensitive learning.