{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:39:06Z","timestamp":1760708346974},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2012,2]]},"abstract":"<jats:p> Na\u00efve Bayes is a simple and efficient classification algorithm which performs well on text classification, which is also known as text categorization. Many researches have been done to improve the performance of the na\u00efve Bayes classifier by weighting the correlated terms, in order to relax the strong assumption of independence between terms. In this paper, we first introduce a new \u03c7<jats:sup>2<\/jats:sup> statistical data, denoted by R<jats:sub>w,c<\/jats:sub>, which can measure positive term-class dependency accurately, and then propose a new weighted na\u00efve Bayes classifier using R<jats:sub>w,c<\/jats:sub> at the training phase. Experimental results with real data sets show that our weighted na\u00efve Bayes classifier has much better performance than the basic na\u00efve Bayes classifier in most cases. <\/jats:p>","DOI":"10.1142\/s0218213011004769","type":"journal-article","created":{"date-parts":[[2011,9,21]],"date-time":"2011-09-21T08:01:46Z","timestamp":1316592106000},"page":"1250008","source":"Crossref","is-referenced-by-count":14,"title":["WEIGHTED NA\u00cfVE BAYES FOR TEXT CLASSIFICATION USING POSITIVE TERM-CLASS DEPENDENCY"],"prefix":"10.1142","volume":"21","author":[{"given":"YANJUN","family":"LI","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, Bronx, New York 10458, USA"}]},{"given":"CONGNAN","family":"LUO","sequence":"additional","affiliation":[{"name":"Teradata Corporation, San Diego, California 92127, USA"}]},{"given":"SOON M.","family":"CHUNG","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Wright State University, Dayton, Ohio 45435, USA"}]}],"member":"219","published-online":{"date-parts":[[2012,4,5]]},"reference":[{"key":"rf1","first-page":"1659","volume":"8","author":"Boulle M.","journal-title":"J. of Machine Learning Research"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007465528199"},{"key":"rf10","first-page":"1457","volume":"18","author":"Kim S.","journal-title":"IEEE Trans. on Knowledge and Data Engineering"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6136-2"},{"key":"rf17","volume-title":"Foundations of Statistical Natural Language Processing","author":"Manning C.","year":"1999"},{"key":"rf19","volume-title":"Machine Learning","author":"Mitchell T.","year":"1997"},{"key":"rf23","first-page":"81","volume":"1","author":"Quinlan J. R.","journal-title":"Machine Learning"},{"key":"rf25","volume-title":"Information Retrieval","author":"van Rijsbergen C. J.","year":"1979"},{"key":"rf26","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"rf27","first-page":"641","volume":"20","author":"Li Y.","journal-title":"IEEE Trans. on Knowledge and Data Engineering"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213011004769","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T16:42:23Z","timestamp":1565109743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213011004769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2]]},"references-count":10,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2012,4,5]]},"published-print":{"date-parts":[[2012,2]]}},"alternative-id":["10.1142\/S0218213011004769"],"URL":"https:\/\/doi.org\/10.1142\/s0218213011004769","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,2]]}}}