THE IMPACT OF FEATURE SELECTION: A DATA-MINING APPLICATION IN DIRECT MARKETING

Ding Wen Tan, William Yeoh, Yee Ling Boo, Soung Yue Liew

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The capability of identifying customers who are more likely to respond to a product is an important issue in direct marketing. This paper investigates the impact of feature selection on predictive models which predict reordering demand of small and medium-sized enterprise customers in a large online job-advertising company. Three well-known feature subset selection techniques in data mining, namely correlation-based feature selection (CFS), subset consistency (SC) and symmetrical uncertainty (SU), are applied in this study. The results show that the predictive models using SU outperform those without feature selection and those with the CFS and SC feature subset evaluators. This study has examined and demonstrated the significance of applying the feature-selection approach to enhance the accuracy of predictive modelling in a direct-marketing context. Copyright © 2013 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)23-38
Number of pages16
JournalIntelligent Systems in Accounting, Finance and Management
Volume20
Issue number1
DOIs
Publication statusPublished - Jan 2013

Keywords

  • correlation-based feature selection
  • data-mining application
  • direct marketing
  • subset consistency
  • symmetrical uncertainty

Fingerprint

Dive into the research topics of 'THE IMPACT OF FEATURE SELECTION: A DATA-MINING APPLICATION IN DIRECT MARKETING'. Together they form a unique fingerprint.

Cite this