Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/23251
Author(s): | Barraza, N. R. Moro, S. Ferreyra, M. de la Peña, A. |
Date: | 2016 |
Title: | Information theory based feature selection for customer classification |
Pages: | 1 - 8 |
Event title: | XVII Argentine Symposium on Artificial Intelligence (ASAI 2016) |
ISSN: | 2451-7585 |
Keywords: | Customer segmentation Feature selection Mutual information |
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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRN - Comunicações a conferências nacionais |
Files in This Item:
File | Description | Size | Format | |
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conferenceobject_29889.pdf | Versão Editora | 312,16 kB | Adobe PDF | View/Open |
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