{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T20:29:58Z","timestamp":1648672198031},"reference-count":3,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2008,6]]},"abstract":"<jats:p> Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in most semi-supervised approaches. We empirically show that the classification performance of two semi-supervised algorithms, self-learning and co-training, improves with the use of our new bridging algorithm in comparison to using the standard classifier, JRipper. We propose a similarity metric for short texts and also study the performance of self-learning with a number of instance selection heuristics. <\/jats:p>","DOI":"10.1142\/s0218213008003972","type":"journal-article","created":{"date-parts":[[2008,6,24]],"date-time":"2008-06-24T09:38:40Z","timestamp":1214300320000},"page":"415-431","source":"Crossref","is-referenced-by-count":3,"title":["SEMI-SUPERVISED CLASSIFICATION USING BRIDGING"],"prefix":"10.1142","volume":"17","author":[{"given":"JASON","family":"CHAN","sequence":"first","affiliation":[{"name":"School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia"}]},{"given":"IRENA","family":"KOPRINSKA","sequence":"additional","affiliation":[{"name":"School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia"}]},{"given":"JOSIAH","family":"POON","sequence":"additional","affiliation":[{"name":"School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf3","volume-title":"Data Mining: Practical Machine Learning Tools with Java Implementations","author":"Frank E.","year":"2005"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"rf9","volume-title":"Introduction to Data Mining","author":"Tan P. N.","year":"2005"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213008003972","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T17:09:48Z","timestamp":1565197788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213008003972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,6]]},"references-count":3,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2008,6]]}},"alternative-id":["10.1142\/S0218213008003972"],"URL":"https:\/\/doi.org\/10.1142\/s0218213008003972","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,6]]}}}