Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study

Abstract:Feature selection is a highly relevant task in a data-driven knowledge discovery project. Several techniques have been developed aiming at finding the features that influence most an outcome to predict, including mutual information and, in recent years, the data-based sensitivity analysis. The present research focus on analyzing the advantages and disadvantages of each of these two techniques, by applying both to a bank telemarketing case. Thereafter, 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 mutual information and 9 features for the data-based sensitivity analysis. The latter performs better for lower values of false positives while the former is slightly better for a higher false positive ratio. Thus, mutual information becomes a better choice if bank managers intend to reduce slightly the cost of contacts without risking losing a high number of successes. Such results show that mutual information, although not recent, is still a valid method for feature selection. On the other side, the data-based sensitivity analysis selection achieved good prediction results with less features.

Machine Learning (cs.LG); Probability (math.PR)

arXiv:2608.20447 [cs.LG]

(or arXiv:2608.20447v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.20447 arXiv-issued DOI via DataCite (pending registration)

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

https://doi.org/10.1177/0165551518770967 DOI(s) linking to related resources