A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing

Abstract

The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub-problem within the original problem. We propose a divide-and-conquer strategy for data mining using both the data-based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate feature. The inbound telemarketing sub-problem re-evaluation led to a large increase in targeting performance, confirming the benefits of such approach and considering the importance of telemarketing for business, in particular in bank marketing.