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This paper investigates the impact of feature selection on predictive models which predict reordering demand of small and medium\u2010sized enterprise customers in a large online job\u2010advertising company. Three well\u2010known feature subset selection techniques in data mining, namely correlation\u2010based 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\u2010selection approach to enhance the accuracy of predictive modelling in a direct\u2010marketing context. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/isaf.1335","type":"journal-article","created":{"date-parts":[[2013,3,6]],"date-time":"2013-03-06T03:54:46Z","timestamp":1362542086000},"page":"23-38","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["THE IMPACT OF FEATURE SELECTION: A DATA\u2010MINING APPLICATION IN DIRECT MARKETING"],"prefix":"10.1002","volume":"20","author":[{"given":"Ding\u2010Wen","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Physical and Mathematical Science, Faculty of Science Universiti Tunku Abdul Rahman, Jalan Universiti  Bandar Barat Kampar Perak 31900 Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Yeoh","sequence":"additional","affiliation":[{"name":"School of Information Systems, Faculty of Business and Law Deakin University  70 Elgar Road Burwood Victoria 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