Predicting the success of bank telemarketing using deep convolutional neural network

Kee Hoon Kim; Chang Seok Lee; Sang Muk Jo; Sung Bae Cho

doi:10.1109/SOCPAR.2015.7492828

Predicting the success of bank telemarketing using deep convolutional neural network

Kee Hoon Kim, Chang Seok Lee, Sang Muk Jo, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Citations (Scopus)

Abstract

Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. The number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.

Original language	English
Title of host publication	Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
Editors	Mario Koppen, Azah Kamilah Muda, Kun Ma, Bing Xue, Hideyuki Takagi, Ajith Abraham
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	314-317
Number of pages	4
ISBN (Electronic)	9781467393607
DOIs	https://doi.org/10.1109/SOCPAR.2015.7492828
Publication status	Published - 2016 Jun 15
Event	7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 - Fukuoka, Japan Duration: 2015 Nov 13 → 2015 Nov 15

Publication series

Name	Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015

Other

Other	7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
Country/Territory	Japan
City	Fukuoka
Period	15/11/13 → 15/11/15

Bibliographical note

Funding Information:
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) sup-port program (IITP-2015-R0992-15-1011)

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Signal Processing
Control and Optimization
Modelling and Simulation

Access to Document

10.1109/SOCPAR.2015.7492828

Cite this

Kim, K. H., Lee, C. S., Jo, S. M., & Cho, S. B. (2016). Predicting the success of bank telemarketing using deep convolutional neural network. In M. Koppen, A. K. Muda, K. Ma, B. Xue, H. Takagi, & A. Abraham (Eds.), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 (pp. 314-317). [7492828] (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SOCPAR.2015.7492828

Kim, Kee Hoon ; Lee, Chang Seok ; Jo, Sang Muk et al. / Predicting the success of bank telemarketing using deep convolutional neural network. Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. editor / Mario Koppen ; Azah Kamilah Muda ; Kun Ma ; Bing Xue ; Hideyuki Takagi ; Ajith Abraham. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 314-317 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015).

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abstract = "Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. The number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.",

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Kim, KH, Lee, CS, Jo, SM & Cho, SB 2016, Predicting the success of bank telemarketing using deep convolutional neural network. in M Koppen, AK Muda, K Ma, B Xue, H Takagi & A Abraham (eds), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015., 7492828, Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Institute of Electrical and Electronics Engineers Inc., pp. 314-317, 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Fukuoka, Japan, 15/11/13. https://doi.org/10.1109/SOCPAR.2015.7492828

Predicting the success of bank telemarketing using deep convolutional neural network. / Kim, Kee Hoon; Lee, Chang Seok; Jo, Sang Muk et al.
Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. ed. / Mario Koppen; Azah Kamilah Muda; Kun Ma; Bing Xue; Hideyuki Takagi; Ajith Abraham. Institute of Electrical and Electronics Engineers Inc., 2016. p. 314-317 7492828 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Predicting the success of bank telemarketing using deep convolutional neural network

AU - Kim, Kee Hoon

AU - Lee, Chang Seok

AU - Jo, Sang Muk

AU - Cho, Sung Bae

N1 - Funding Information: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) sup-port program (IITP-2015-R0992-15-1011) Publisher Copyright: © 2015 IEEE.

PY - 2016/6/15

Y1 - 2016/6/15

N2 - Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. The number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.

AB - Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. The number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.

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M3 - Conference contribution

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BT - Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015

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Kim KH, Lee CS, Jo SM, Cho SB. Predicting the success of bank telemarketing using deep convolutional neural network. In Koppen M, Muda AK, Ma K, Xue B, Takagi H, Abraham A, editors, Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 314-317. 7492828. (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015). doi: 10.1109/SOCPAR.2015.7492828

Predicting the success of bank telemarketing using deep convolutional neural network

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