{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:39:16Z","timestamp":1787027956089,"version":"build-2736575974"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,5,25]],"date-time":"2017-05-25T00:00:00Z","timestamp":1495670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e19060247","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T04:35:42Z","timestamp":1496118942000},"page":"247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy"],"prefix":"10.3390","volume":"19","author":[{"given":"Joaqu\u00edn","family":"Abell\u00e1n","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Castellano","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1109\/TKDE.2003.1245283","article-title":"Benchmarking attribute selection techniques for discrete class data mining","volume":"15","author":"Hall","year":"2003","journal-title":"IEEE Trans. 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