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
Data Warehousing, Data Mining, and Olap
Data Warehousing, Data Mining, and Olap
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
Feature representation for customer attrition risk prediction in retail banking
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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Nowadays customer attrition is increasingly serious in commercial banks, particularly, high-valued customers in retail banking. Hence, it is encouraged to develop a prediction mechanism and identify such 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 previous studies, a number of techniques have been introduced in (binary) classification study, i. e. artificial-based model, Bayesian-based model, case-based model, tree-based model, regression-based model, rule-based model, etc. With regards to a particular application --- predicting customer attrition risk for retail banking, this paper presents four principles in (classification) model selection. To support this model selection study, a set of experiments were run, based on a collection of real customer data in retail banking. These results and consequent recommendations are given in this paper.