Feature selection is a highly relevant task in any data-driven knowledge discovery project. The present research focus 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. On the other side, DSA achieved good prediction results with less features.

Highlights We present a mathematical model based on a new stochastic process described by a Pure Birth process. The proposed model matches the subexponential growth on the early stage of an epidemic. The mathematical expression of the cumulative case incidence and cumulative death curves is obtained, with a quite accurate fit in both cases. The model contains two parameters, the immunization and infection rates. The behavior in time of those parameters allows to assess the evolution of the outbreak. We obtain a new indicator, the mean time between infections. This indicator allows not only to monitor the epidemic growth but also to predict the peak of cases.

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