A dynamic ensemble selection method for bank telemarketing sales prediction

Abstract

We propose a dynamic ensemble selection method, META-DES-AAP, to predict the success of bank telemarketing sales of time deposits. Unlike existing machine learning-based marketing sales prediction methods focusing only on prediction accuracy, META-DES-AAP considers the accuracy and average profit maximization. In META-DES-AAP, to consider both accuracy and average profit in the framework of dynamic ensemble selection using meta-training, a multi-objective optimization algorithm is designed to maximize the accuracy and average profit for base classifiers selection. Base classifiers suitable for each test telemarketing campaign are integrated according to the dynamic-based base classifiers integration method. Experimental results on bank telemarketing data show that META-DES-AAP achieves the best accuracy and the largest average profit when compared across several state-of-the-art machine learning methods. In addition, the factors influencing telemarketing on the average predicted probability of telemarketing success and average profit obtained by META-DES-AAP are analyzed.