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

Abd Al-Aziz Hosni El-Bagoury¹, Sundus Naji AL-Aziz^2*, S.S.ASKAR³

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

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

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

Emails: azizhel2013@yahoo.com;  snalaziz@pnu.edu.sa ; saskar@ksu.edu.sa

^*Corresponding Author: Sundus Naji AL-Aziz. Email: Sundus.alaziz3@gmail.com

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