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American Journal of Business and Operations Research
Volume 7 , Issue 1, PP: 09-18 , 2022 | Cite this article as | XML | Html |PDF

Title

Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector

Authors Names :   Abd Al-Aziz Hosni El-Bagoury   1 *     Sundus Naji AL-Aziz   2     S.S.ASKAR   3  

1  Affiliation :  Higher Institute of Engineering and Technology, El-Mahala El-Kobra, Egypt

    Email :  azizhel2013@yahoo.com


2  Affiliation :  Department of Mathematics, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, KSA

    Email :  snalaziz@pnu.edu.sa


3  Affiliation :  Faculty of Science, King Saud University, Riyadh, Saudi Arabia

    Email :  saskar@ksu.edu.sa



Doi   :   https://doi.org/10.54216/AJBOR.070101

Received: January 11, 2022 Accepted: May 20, 2022

Abstract :

Telemarketing becomes a major tool in enhancing the services of different business sectors. On banking industry, telemarketing is applied to sell products or services. Banking advertisements as well as marketing are majorly based on the detailed information of neutral data related to marketing market and original needs of user for the banks. Decision making becomes an essential part in the telemarketing field that computes a particular class of automated fact in assisting the companies for making decision. Artificial intelligence (AI) is applied for decision making in the telemarketing sector. In this aspect, this paper introduces a social spider optimization (SSOA) with gradient boosting tree (GBT) model for decision making in the telemarketing sector. The main aim of the SSOA-GBT method is to make proper decisions in the telemarketing sectors. To accomplish this, the SSOA-GBT model initially exploits the GBT model for data classification purposes. Next, for improving the performance of the GBT classifier, the SSOA is applied. The performance validation of the SSOA-GBT model is performed using benchmark dataset and the outcomes are investigated in several aspects. The simulation outcomes indicated the better outcomes of the SSOA-GBT approach over the recent approaches

Keywords :

Telemarketing; Banking sector; Decision making; Social spider optimization; Gradient boosting tree

References :

[1]   Rahayu, M., Rasid, F. and Tannady, H., 2018. Effects of self efficacy, job satisfaction, and work culture toward performance of telemarketing staff in banking sector. South East Asia J. Contemp. Business, Econ. Law, 16(5), pp.47-52.

[2]   Farooqi, R. and Iqbal, N., 2019. Performance evaluation for competency of bank telemarketing prediction using data mining techniques. International Journal of Recent Technology and Engineering, 8(2), pp.5666-5674.

[3]   Prompreing, K. and Prompreing, T., 2021. A Telemarketing Guidance in Selling Banking Services: A Data Mining Approach. Indonesian Journal of Business Analytics, 1(1), pp.1-16.

[4]   Prompreing, K. and Prompreing, T., 2021. A Telemarketing Guidance in Selling Banking Services: A Data Mining Approach. Indonesian Journal of Business Analytics, 1(1), pp.1-16.

[5]   Desai, R., 2021. Performance Enhancement of Hybrid Algorithm for Bank Telemarketing. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(8), pp.2703-2711.

[6]   Borugadda, P., Nandru, P. and Madhavaiah, C., 2021. Predicting the Success of Bank Telemarketing for Selling Long-term Deposits: An Application of Machine Learning Algorithms. St. Theresa Journal of Humanities and Social Sciences, 7(1), pp.91-108.

[7]   KarakuĊŸ, M.Ö., 2021. A Multi-Layer Neural Network Approach to Predict The Success of Bank Telemarketing. Artificial Intelligence Theory and Applications, 1(1).

[8]   Puteri, A.N., Arizal, A. and Achmad, A.D., 2021. Feature Selection Correlation-Based pada Prediksi Nasabah Bank Telemarketing untuk Deposito. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 20(2), pp.335-342.

[9]   Tékouabou Koumétio, S.C. and Toulni, H., 2021. Improving KNN Model for Direct Marketing Prediction in Smart Cities. In Machine Intelligence and Data Analytics for Sustainable Future Smart Cities (pp. 107-118). Springer, Cham.

[10] Ilham, A., Khikmah, L. and Iswara, I.B.A.I., 2019, March. Long-term deposits prediction: a comparative framework of classification model for predict the success of bank telemarketing. In Journal of Physics: Conference Series (Vol. 1175, No. 1, p. 012035). IOP Publishing.

[11] Asif, M., 2018. Predicting the Success of Bank Telemarketing using various Classification Algorithms.

[12] Sun, Y., Cheng, X., Li, H., Lin, Y., Sung, J. and Wang, A., 2019. Prediction of Bank Customer Telemarketing Success Using Data Mining Models. ADRRI Journal of Engineering and Technology, 4(5), pp.18-32. 

[13] Che, J., Zhao, S., Li, Y. and Li, K., 2020. Bank Telemarketing Forecasting Model Based on t-SNE-SVM. Journal of Service Science and Management, 13(3), pp.435-448. 

[14] Kim, K.H., Lee, C.S., Jo, S.M. and Cho, S.B., 2015, November. Predicting the success of bank telemarketing using deep convolutional neural network. In 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) (pp. 314-317). IEEE. 

[15] Ghatasheh, N., Faris, H., AlTaharwa, I., Harb, Y. and Harb, A., 2020. Business analytics in telemarketing: cost-sensitive analysis of bank campaigns using artificial neural networks. Applied Sciences, 10(7), p.2581. 

[16] Turkmen, E., 2021, February. Deep Learning Based Methods for Processing Data in Telemarketing-Success Prediction. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 1161-1166). IEEE.

[17] Zhang, Y. and Haghani, A., 2015. A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, pp.308-324.

[18] Rao, H., Shi, X., Rodrigue, A.K., Feng, J., Xia, Y., Elhoseny, M., Yuan, X. and Gu, L., 2019. Feature selection based on artificial bee colony and gradient boosting decision tree. Applied Soft Computing, 74, pp.634-642.

[19] Cuevas, E., Cienfuegos, M., Zaldívar, D. and Pérez-Cisneros, M., 2013. A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), pp.6374-6384.

[20] Luque-Chang, A., Cuevas, E., Fausto, F., Zaldivar, D. and Pérez, M., 2018. Social spider optimization algorithm: modifications, applications, and perspectives. Mathematical Problems in Engineering, 2018.

[21] https://www.kaggle.com/janiobachmann/bank-marketing-dataset

[22] https://archive.ics.uci.edu/ml/datasets/Bank+Marketing

[23] Noura Metawa, Amany Ahmed Elshimy, “Parameter Tuned Machine Learning based Decision Support System for Bank Telemarketing”, American Journal of Business and Operations Research (AJBOR), Vol. 4, No. 1, PP. 28-38, 2021


Cite this Article as :
Style #
MLA Abd Al-Aziz Hosni El-Bagoury,Sundus Naji AL-Aziz,S.S.ASKAR. "Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector." American Journal of Business and Operations Research, Vol. 7, No. 1, 2022 ,PP. 09-18.
APA Abd Al-Aziz Hosni El-Bagoury,Sundus Naji AL-Aziz,S.S.ASKAR. (2022). Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector. American Journal of Business and Operations Research, 7 ( 1 ), 09-18.
Chicago Abd Al-Aziz Hosni El-Bagoury,Sundus Naji AL-Aziz,S.S.ASKAR. "Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector." American Journal of Business and Operations Research, 7 no. 1 (2022): 09-18.
Harvard Abd Al-Aziz Hosni El-Bagoury,Sundus Naji AL-Aziz,S.S.ASKAR. (2022). Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector. American Journal of Business and Operations Research, 7 ( 1 ), 09-18.
Vancouver Abd Al-Aziz Hosni El-Bagoury,Sundus Naji AL-Aziz,S.S.ASKAR. Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector. American Journal of Business and Operations Research, (2022); 7 ( 1 ): 09-18.
IEEE Abd Al-Aziz Hosni El-Bagoury,Sundus Naji AL-Aziz,S.S.ASKAR, Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector, American Journal of Business and Operations Research, Vol. 7 , No. 1 , (2022) : 09-18 (Doi   :  https://doi.org/10.54216/AJBOR.070101)