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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Barraza, N., Moro, S., Ferreyra, M. & de la Peña, A. (2019). Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study. Journal of Information Science. 45 (1), 53-67
Exportar Referência (IEEE) 
N. R. Barraza et al.,  "Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study", in Journal of Information Science, vol. 45, no. 1, pp. 53-67, 2019
Exportar BibTeX 
@article{barraza2019_1714062371550,
	author = "Barraza, N. and Moro, S. and Ferreyra, M. and de la Peña, A.",
	title = "Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study",
	journal = "Journal of Information Science",
	year = "2019",
	volume = "45",
	number = "1",
	doi = "10.1177/0165551518770967",
	pages = "53-67",
	url = "http://journals.sagepub.com/doi/10.1177/0165551518770967"
}
Exportar RIS 
TY  - JOUR
TI  - Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study
T2  - Journal of Information Science
VL  - 45
IS  - 1
AU  - Barraza, N.
AU  - Moro, S.
AU  - Ferreyra, M.
AU  - de la Peña, A.
PY  - 2019
SP  - 53-67
SN  - 0165-5515
DO  - 10.1177/0165551518770967
UR  - http://journals.sagepub.com/doi/10.1177/0165551518770967
AB  - 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.
ER  -