Profit-based feature selection using support vector machines - general framework and an application for customer retention
Profit-based feature selection using support vector machines - general framework and an application for customer retention
Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables. Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on support vector machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals.
data mining, feature selection, support vector machines, churn prediction, customer retention, maximum profit
1-9
Maldonado, Sebastián
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Flores, Álvaro
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Verbraken, Thomas
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Baesens, Bart
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Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Flores, Álvaro
762a13d4-a66c-4fb2-9552-996f25855674
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Maldonado, Sebastián, Flores, Álvaro, Verbraken, Thomas, Baesens, Bart and Weber, Richard
(2015)
Profit-based feature selection using support vector machines - general framework and an application for customer retention.
Applied Soft Computing, .
(doi:10.1016/j.asoc.2015.05.058).
Abstract
Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables. Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on support vector machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals.
Text
Maldonado_Profit.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 16 May 2015
e-pub ahead of print date: 4 July 2015
Keywords:
data mining, feature selection, support vector machines, churn prediction, customer retention, maximum profit
Organisations:
Southampton Business School
Identifiers
Local EPrints ID: 378955
URI: http://eprints.soton.ac.uk/id/eprint/378955
ISSN: 1568-4946
PURE UUID: 6853a9d3-611d-446d-9df4-34e4cd12470a
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Date deposited: 15 Jul 2015 09:14
Last modified: 15 Mar 2024 03:20
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Contributors
Author:
Sebastián Maldonado
Author:
Álvaro Flores
Author:
Thomas Verbraken
Author:
Richard Weber
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