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Article

Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector

Department of Information Technology, Cape Peninsula University of Technology, Cape Town PO Box 8000, South Africa
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Academic Editors: Ali Cemal Benim and Demos T. Tsahalis
Received: 7 November 2020 / Revised: 17 February 2021 / Accepted: 19 February 2021 / Published: 16 March 2021
Until recently, traditional machine learning techniques (TMLTs) such as multilayer perceptrons (MLPs) and support vector machines (SVMs) have been used successfully for churn prediction, but with significant efforts expended on the configuration of the training parameters. The selection of the right training parameters for supervised learning is almost always experimentally determined in an ad hoc manner. Deep neural networks (DNNs) have shown significant predictive strength over TMLTs when used for churn predictions. However, the more complex architecture of DNNs and their capacity to process huge amounts of non-linear input data demand more time and effort to configure the training hyperparameters for DNNs during churn modeling. This makes the process more challenging for inexperienced machine learning practitioners and researchers. So far, limited research has been done to establish the effects of different hyperparameters on the performance of DNNs during churn prediction. There is a lack of empirically derived heuristic knowledge to guide the selection of hyperparameters when DNNs are used for churn modeling. This paper presents an experimental analysis of the effects of different hyperparameters when DNNs are used for churn prediction in the banking sector. The results from three experiments revealed that the deep neural network (DNN) model performed better than the MLP when a rectifier function was used for activation in the hidden layers and a sigmoid function was used in the output layer. The performance of the DNN was better when the batch size was smaller than the size of the test set data, while the RemsProp training algorithm had better accuracy when compared with the stochastic gradient descent (SGD), Adam, AdaGrad, Adadelta, and AdaMax algorithms. The study provides heuristic knowledge that could guide researchers and practitioners in machine learning-based churn prediction from the tabular data for customer relationship management in the banking sector when DNNs are used. View Full-Text
Keywords: churn prediction; churn modeling; machine learning; deep neural networks; supervised learning; customer relationship management churn prediction; churn modeling; machine learning; deep neural networks; supervised learning; customer relationship management
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MDPI and ACS Style

Domingos, E.; Ojeme, B.; Daramola, O. Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector. Computation 2021, 9, 34. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030034

AMA Style

Domingos E, Ojeme B, Daramola O. Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector. Computation. 2021; 9(3):34. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030034

Chicago/Turabian Style

Domingos, Edvaldo, Blessing Ojeme, and Olawande Daramola. 2021. "Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector" Computation 9, no. 3: 34. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030034

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