In this paper, we present a data mining approach for analyzing retailing bank customer attrition. We discuss the challenging issues such as highly skewed data, time series data unrolling, leaker field detection etc, and the procedure of a data mining project for the attrition analysis for retailing bank. We explain the advantages of lift as a proper measure for attrition analysis and compare the lift of data mining models of decision tree, boosted naïve Bayesian network, selective Bayesian network, neural network and the ensemble of classifiers of the above methods. Some interesting findings are reported. Our research work demonstrates the effectiveness and efficiency of data mining in attrition analysis for retailing bank.
CITATION STYLE
Hu, X. (2002). Comparison of classification methods for customer attrition analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2475, pp. 487–492). Springer Verlag. https://doi.org/10.1007/3-540-45813-1_64
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