Volume 9 , Issue 1 , PP: 27-39, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mohamed Elsharkawy 1 * , I.S. Farahat 2
Doi: https://doi.org/10.54216/JCIM.090103
Bank telemarketing is a prominent way of direct marketing approach in which the telemarketers ask possible clients by mobile phones for purchasing or subscribing to bank product or service. But the clients who are not interested in the offers or promotions by the bank telemarketing commonly face negative interaction owing to the thought of thinking the telemarketing as spam. Therefore, the recent developments of deep learning (DL) models can be used to realize the predictive models for bank telemarketing applications. This study develops an effective Archimedes Optimization with Deep Belief Network based Predictive (AOA-DBNP) for bank telemarketing applications. The proposed AOA-DBNP technique primarily undergoes pre-processing for transforming the data as to useful format. In addition, the AOA-DBNP technique involves the use of the DBN model for the prediction process and finally, the AOA is applied for tuning the hyperparameters of DBN technique. The utilization of AOA helps to optimally select the hyperparameters of the DBN model in such a way that the predictive performance gets improved to a maximum extent. To showcase the enhanced efficiency of the AOA-DBNP manner, a comprehensive comparative results analysis reported the better performance of the AOA-DBNP model.
Bank telemarketing, Deep learning, Business section, Parameter tuning, Data mining
[1] Moro, S.; Cortez, P.; Rita, P. A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 2014, 62, 22–31.
[2] Frempong, A.J.; Jayabalan, M. Predicting customer response to bank direct telemarketing campaign. In Proceedings of the 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, Malaysia, 18–20 September 2017; pp. 1–4.
[3] Ghatasheh, N.; Faris, H.; AlTaharwa, I.; Harb, Y.; Harb, A. Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks. Appl. Sci. 2020, 10, 2581.
[4] LEE, H. A Method of Bank Telemarketing Customer Prediction based on Hybrid Sampling and Stacked Deep Networks. J. Korea Soc. Digit. Ind. Inf. Manag. 2019, 15, 197–206.
[5] Moro, S.; Cortez, P.; Rita, P. A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing. Expert Syst. 2018, 35, e12253.
[6] Arenas-Márquez, F.J.; Martinez-Torres, R.; Toral, S. Convolutional neural encoding of online reviews for the identification of travel group type topics on TripAdvisor. Inf. Process. Manag. 2021, 58, 102645.
[7] Fu, X.; Ouyang, T.; Chen, J.; Luo, X. Listening to the investors: A novel framework for online lending default prediction using deep learning neural networks. Inf. Process. Manag. 2020, 57, 102236.
[8] Neagoe, V.E.; Ciotec, A.D.; Cucu, G.S. Deep convolutional neural networks versus multilayer perceptron for financial prediction. In Proceedings of the 2018 International Conference on Communications (COMM), Bucharest, Romania, 14–16 June 2018; pp. 201–206.
[9] Song, C.; Ning, N.; Zhang, Y.; Wu, B. A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Inf. Process. Manag. 2021, 58, 102437
[10] Gu, J., Na, J., Park, J. and Kim, H., 2021. Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network. Applied Sciences, 11(15), p.7147.
[11] Selma, M., 2020. Predicting the Success of Bank Telemarketing Using Artificial Neural Network. International Journal of Economics and Management Engineering, 14(1), pp.1-4.
[12] Moro, S., Cortez, P. and Rita, P., 2014. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, pp.22-31.
[13] Hosseini, S., 2021. A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing. Journal of Management Analytics, 8(2), pp.295-315.
[14] Asif, M., 2018. Predicting the Success of Bank Telemarketing using various Classification Algorithms.
[15] Keles, A. and Keles, A., 2015. IBMMS Decision Support Tool for Management of Bank Telemarketing Campaigns. arXiv preprint arXiv:1511.03532.
[16] Pradap, R. and Kamaludeen, P., 2019. Machine Learning Modelsfor Bank Telemarketing Classification and Prediction.
[17] Feng, Y., Yin, Y., Wang, D. and Dhamotharan, L., 2022. A dynamic ensemble selection method for bank telemarketing sales prediction. Journal of Business Research, 139, pp.368-382.
[18] Yan, C., Li, M. and Liu, W., 2020. Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network. Applied Soft Computing, 92, p.106259.
[19] Bolloju, N., Khalifa, M. and Turban, E., 2002. Integrating knowledge management into enterprise environments for the next generation decision support. Decision Support Systems, 33(2), pp.163-176.
[20] Rosa G, et al. Learning parameters in deep belief networks through firefly algorithm. In: IAPR workshop on artificial neural networks in pattern recognition. Springer; 2016.
[21] Sun, X., Wang, G., Xu, L., Yuan, H. and Yousefi, N., 2021. Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm. Energy, 237, p.121532.
[22] https://www.kaggle.com/janiobachmann/bank-marketing-dataset
