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    Title: Customer churn prediction by hybrid neural networks
    Authors: 蔡志豐;Tsai, Chih-Fong;盧鈺欣;Lu, Yu-Hsin
    Contributors: 會計與資訊學系
    Keywords: Hybrid data mining;Self-organizing maps;Back-propagation neural networks;Churn prediction
    Date: 2009
    Issue Date: 2012-11-26 12:34:12 (UTC+8)
    Abstract: As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.
    Appears in Collections:[Department of Accounting and Information Systems] Journal Article

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