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|Title:||Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study|
de la Peña, A.
|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.|
|Appears in Collections:||ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica|
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|2018-JIS-BarrazaMoroEtAl-PosPrint.pdf||Pós-print||677.99 kB||Adobe PDF||View/Open|
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