A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Principles of data mining
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Improving classifier performance by knowledge-driven data preparation
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Model selection strategy for customer attrition risk prediction in retail banking
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Nowadays, customer attrition is increasingly serious in commercial banks, particularly with respect tomiddle- and high-valued customers in retail banking. To combat this attrition it is incumbent for banks to develop a prediction mechanism so as to identify customers who might be at risk of attrition. This prediction mechanism can be considered to be a classifier. In particular, the problem of predicting risk of customer attrition can be prototyped as a binary classification task in data mining. In this paper we identify a set of features, for customer "attrition vs. non-attrition" classification, based on the RFM (Recency, Frequency and Monetary) model. The reported evaluation indicates that proposed set of features produces a much more effective classifier than that generated using previously suggested features.