%0 Journal Article
%T Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study
%A Adolfo de la PeŁża
%A Marcelo Ferreyra
%A N¨¦stor Barraza
%A S¨¦rgio Moro
%J Journal of Information Science
%@ 1741-6485
%D 2019
%R 10.1177/0165551518770967
%X 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
%K Customer targeting
%K direct marketing
%K feature selection
%K modelling
%K mutual information
%K sensitivity analysis
%U https://journals.sagepub.com/doi/full/10.1177/0165551518770967