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

Sérgio Moro, Paulo Cortez, Paulo Rita

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Article numbere12253
JournalExpert Systems
Volume35
Issue number3
Early online date2017
DOIs
Publication statusPublished - 8 Jun 2018

Keywords

  • Banking
  • Data mining
  • Divide and conquer
  • Feature selection
  • Marketing

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