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
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Adaptive mixtures of local experts
Neural Computation
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
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Telecommunications companies and financial institutions are facing increasing competition. A staged preprocessing framework for cost-sensitive-data processing can help these companies identify customers who might switch to a competitor (or churn). The framework gives users an intuitive idea of the data distribution using a self-organizing map and then uses a cost matrix to help convert the data with an improved equidepth discretization method. The preprocessed data set can be input to any classifier. When tested on the KDD Cup 1998 data set, the framework performed better than the competition's winner. It has also been implemented in a software product called ED-Money and applied to a Chinese mobile telecommunication data set.