Article2013

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

Ding‐Wen Tan · William Yeoh · Yee Ling Boo · Soung‐Yue Liew

8 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

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Customer churn and segmentation Data Mining and Machine Learning Applications Data Mining Algorithms and Applications

Abstract

SUMMARY 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.

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References

1999Data preparation for data mining 2008Application of data mining techniques in customer relationship management: A literature review and classification 2004Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 2008Quantitative models for direct marketing: A review from systems perspective 2002Business applications of data mining 2003An intelligent system for customer targeting: a data mining approach 2001Wrapped input selection using multilayer perceptrons for repeat‐purchase modeling in direct marketing 2005Constructing response model using ensemble based on feature subset selection

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