Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/16227
Author(s): | Barraza, N. Moro, S. Ferreyra, M. de la Peña, A. |
Date: | 2019 |
Title: | Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study |
Volume: | 45 |
Number: | 1 |
Pages: | 53 - 67 |
ISSN: | 0165-5515 |
DOI (Digital Object Identifier): | 10.1177/0165551518770967 |
Keywords: | Customer targeting Direct marketing Feature selection Modelling Mutual information Sensitivity analysis |
Abstract: | Feature selection is a highly relevant task in any data-driven knowledge discovery project. The present research focuses on analysing the advantages and disadvantages of using mutual information (MI) and data-based sensitivity analysis (DSA) for feature selection in classification problems, by applying both to a bank telemarketing case. A logistic regression model is built on the tuned set of features identified by each of the two techniques as the most influencing set of features on the success of a telemarketing contact, in a total of 13 features for MI and 9 for DSA. The latter performs better for lower values of false positives while the former is slightly better for a higher false-positive ratio. Thus, MI becomes a better choice if the intention is reducing slightly the cost of contacts without risking losing a high number of successes. However, DSA achieved good prediction results with less features. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Description | Size | Format | |
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2018-JIS-BarrazaMoroEtAl-PosPrint.pdf | Pós-print | 677,99 kB | Adobe PDF | View/Open |
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