{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T04:57:41Z","timestamp":1648875461263},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:p> In spite of its simplicity, naive Bayesian learning has been widely used in many data mining applications. However, the unrealistic assumption that all features are equally important negatively impacts the performance of naive Bayesian learning. In this paper, we propose a new method that uses a Kullback\u2013Leibler measure to calculate the weights of the features analyzed in naive Bayesian learning. Its performance is compared to that of other state-of-the-art methods over a number of datasets. <\/jats:p>","DOI":"10.1142\/s0218001414510070","type":"journal-article","created":{"date-parts":[[2014,6,23]],"date-time":"2014-06-23T02:10:57Z","timestamp":1403489457000},"page":"1451007","source":"Crossref","is-referenced-by-count":2,"title":["AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES"],"prefix":"10.1142","volume":"28","author":[{"given":"CHANG-HWAN","family":"LEE","sequence":"first","affiliation":[{"name":"Department of Information and Communications, Dongguk University, Seoul, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,7,31]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1006559212014"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007413511361"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007465528199"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2006.11.008"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"rf17","volume-title":"C4.5: Programs for Machine Learning","author":"Quinlan J. R.","year":"1993"},{"key":"rf18","first-page":"725","volume":"49","author":"Quinten A.","year":"1999","journal-title":"Edu. Psychol. Meas."},{"key":"rf19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1006593614256"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001414510070","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T04:53:41Z","timestamp":1565153621000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001414510070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,7,31]]},"references-count":11,"journal-issue":{"issue":"05","published-online":{"date-parts":[[2014,7,31]]},"published-print":{"date-parts":[[2014,8]]}},"alternative-id":["10.1142\/S0218001414510070"],"URL":"https:\/\/doi.org\/10.1142\/s0218001414510070","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,7,31]]}}}