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
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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Software Engineering Economics
Software Engineering Economics
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Induction of One-Level Decision Trees
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
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
Predicting Software Escalations with Maximum ROI
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
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
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
Cost-Sensitive Decision Trees with Pre-pruning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Expert Systems with Applications: An International Journal
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
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While most software defects (i.e., bugs) are corrected and tested as part of the lengthy software development cycle, enterprise software vendors often have to release software products before all reported defects are corrected, due to deadlines and limited resources. A small number of these defects will be escalated by customers and they must be resolved immediately by the software vendors at a very high cost. In this paper, we develop an Escalation Prediction (EP) system that mines historic defect report data and predict the escalation risk of the defects for maximum net profit. More specifically, we first describe a simple and general framework to convert the maximum net profit problem to cost-sensitive learning. We then apply and compare several well-known cost-sensitive learning approaches for EP. Our experiments suggest that the cost-sensitive decision tree is the best method for producing the highest positive net profit and comprehensible results. The EP system has been deployed successfully in the product group of an enterprise software vendor.