Parameter Tuned Machine Learning based Decision Support System for Bank Telemarketing

Abstract

In banking sectors, telemarketing is the major support of selling the products or services. Banking advertisement and marketing are mainly depending upon the comprehensive knowledge of objective data regarding the market and the actual client requirements for the bank gainful way. Decision Support Systems (DSS) play a vital part in telemarketing sector, which determines a specific class of automized facts to assist the company to make decisions. Machine learning (ML) is commonly used in the DSS which integrates the data and computer application for precise prediction of results. This paper presents an effective parameter tuned ML based DSS (PTML-DSS) for bank telemarketing sector. The proposed PTML-DSS technique follows a three-level process namely preprocessing, classification, and parameter optimization. Initially, the marketing data is preprocessed to get rid of unwanted information. In addition, gradient boosting decision tree (GBDT) based classifier model is used to classify the data. Besides, firefly algorithm (FFA) is applied for tuning the parameters involved in the GBDT model. In order to verify the improved performance of the PTML-DSS technique, a series of simulations were performed, and the results are inspected under varying aspects. The resultant values reported the improved performance of the PTML-DSS technique over the other techniques.