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Applying feature selection methods to improve the predictive model of a direct marketing problem

Tan, Ding-Wen, Sim, Yee-Wai and Yeoh, William 2011, Applying feature selection methods to improve the predictive model of a direct marketing problem, in Software engineering and computer systems, Springer-Verlag, Heidelberg , Germany, pp.155-167.

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Title Applying feature selection methods to improve the predictive model of a direct marketing problem
Author(s) Tan, Ding-Wen
Sim, Yee-Wai
Yeoh, William
Title of book Software engineering and computer systems
Editor(s) Zain, J. M.
Publication date 2011
Series Communications in computer and information science; v.179, p.4
Chapter number 14
Total chapters 65
Start page 155
End page 167
Total pages 13
Publisher Springer-Verlag
Place of Publication Heidelberg , Germany
Keyword(s) data mining
direct marketing
feature selection
artificial neural networks
decision trees
Summary The ability to forecast job advertisement demands is vital to enhance the customer retention rate for recruitment companies. On top of that, it is uneconomical to cold call every individual on a regular basis for companies with a large pool of customers. This paper presents a novel approach in predicting the re-ordering demand of a potential group of SMEs customers in a large online recruitment company. Two feature selection techniques, namely Correlation-based Feature Selection (CFS) and Subset Consistency (SC) Feature Selection, were applied to predictive models in this study. The predictive models were compared with other similar models in the absence of feature selections. Results of various experiments show that those models using feature selections generally outperform those without feature selections. The results support the authors’ hypothesis that the predictive model can perform better and further ahead than similar methods that exclude feature selection.
Notes Proceedings of the Second International Conference on Software Engineering and Computer Systems [ICSECS 2011], Kuantan, Pahang, Malaysia, June 27-29, 2011
ISBN 9783642221699
3642221696
ISSN 1865-0929
Edition 1st
Language eng
Field of Research 080699 Information Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
Copyright notice ©2011, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30036779

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