Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy Abellán Mulero, Joaquín García Castellano, Francisco Javier Variable selection Classification Naive Bayes Imprecise probabilities Uncertainty measures Variable selection methods play an important role in the field of attribute mining. The Naive Bayes (NB) classifier is a very simple and popular classification method that yields good results in a short processing time. Hence, it is a very appropriate classifier for very large datasets. The method has a high dependence on the relationships between the variables. The Info-Gain (IG) measure, which is based on general entropy, can be used as a quick variable selection method. This measure ranks the importance of the attribute variables on a variable under study via the information obtained from a dataset. The main drawback is that it is always non-negative and it requires setting the information threshold to select the set of most important variables for each dataset. We introduce here a new quick variable selection method that generalizes the method based on the Info-Gain measure. It uses imprecise probabilities and the maximum entropy measure to select the most informative variables without setting a threshold. This new variable selection method, combined with the Naive Bayes classifier, improves the original method and provides a valuable tool for handling datasets with a very large number of features and a huge amount of data, where more complex methods are not computationally feasible. 2018-01-23T08:11:21Z 2018-01-23T08:11:21Z 2017-05-25 info:eu-repo/semantics/article Abellán, J.; García Castellano, F.J. Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy. Entropy, 19(6): 247 (2017). [http://hdl.handle.net/10481/49082] 1099-4300 http://hdl.handle.net/10481/49082 10.3390/e19060247 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License MDPI