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Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy
dc.contributor.author | Abellán Mulero, Joaquín | es_ES |
dc.contributor.author | García Castellano, Francisco Javier | es_ES |
dc.date.accessioned | 2018-01-23T08:11:21Z | |
dc.date.available | 2018-01-23T08:11:21Z | |
dc.date.issued | 2017-05-25 | |
dc.identifier.citation | 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] | es_ES |
dc.identifier.issn | 1099-4300 | |
dc.identifier.uri | http://hdl.handle.net/10481/49082 | |
dc.description.abstract | 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. | en_EN |
dc.description.sponsorship | This work has been supported by the Spanish “Ministerio de Economía y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | es_ES |
dc.subject | Variable selection | en_EN |
dc.subject | Classification | en_EN |
dc.subject | Naive Bayes | en_EN |
dc.subject | Imprecise probabilities | en_EN |
dc.subject | Uncertainty measures | en_EN |
dc.title | Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy | en_EN |
dc.type | info:eu-repo/semantics/article | en_EN |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en_EN |
dc.identifier.doi | 10.3390/e19060247 |