Publication in conference proceedings
Information theory based feature selection for customer classification
Néstor Ruben Barraza (Barraza, N. R.); Sérgio Moro (Moro, S.); Marcelo Ferreyra (Ferreyra, M.); Adolfo de la Peña (de la Peña, A.);
45th JAIIO. Proceedings of ASAI 2016. Simposio Argentino de Inteligencia Artificial
Year (definitive publication)
2016
Language
English
Country
Argentina
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Times Cited: 2

(Last checked: 2024-10-12 20:18)

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Abstract
The application of Information Theory techniques in customer feature selection is analyzed. This method, usually called information gain has been demonstrated to be simple and fast for feature selection. The important concept of mutual information, originally introduced to analyze and model a noisy channel is used in order to measure relations between characteristics of given customers. An application to a bank customers data set of telemarketing calls for selling bank long-term deposits is shown. We show that with our method, 80% of the subscribers can be reached by contacting just the better half of the classified clients.
Acknowledgements
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Keywords
Customer segmentation,Feature selection,Mutual information
Funding Records
Funding Reference Funding Entity
32/15 201 Universidad Nacional de Tres de Febrero