{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T06:08:58Z","timestamp":1776492538314,"version":"3.51.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p> As a prominent semi-supervised learning algorithm, co-training can make full use of a few labeled samples as well as a quantity of unlabeled samples to train robust classifiers, thus it is widely researched in the last decades. However, there are still two severe problems in existing co-training methods: firstly, data used in co-training needs at least two sufficient and redundant views, but few practical datasets can meet it. Secondly, because there are a few labeled samples in the initial stage, the initial classifiers trained by co-training are usually too weak to correctly label unlabeled samples, which may bring in noisy labels for the following training process. To deal with these problems, we propose a co-training algorithm by predicting confidence of unlabeled neighbors (termed LCN-CoTrain). Specifically, LCN-CoTrain first defines an entropy-based view division method to generate redundant views. Meanwhile, LCN-CoTrain introduces a novel labeling confidence prediction strategy, which takes the nearest unlabeled neighbors into consideration when calculating the labeling confidence of a certain sample. To validate the efficiency of our proposed LCN-CoTrain algorithm, we experiment on four UCI datasets and compare LCN-CoTrain with several representative co-training methods. The experimental results indicate that our proposed LCN-CoTrain can learn robust classifiers and outperform most of baseline methods. <\/jats:p>","DOI":"10.1142\/s0218213022400231","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T07:08:25Z","timestamp":1663916905000},"source":"Crossref","is-referenced-by-count":3,"title":["A Co-training Algorithm by Predicting Confidence of Unlabeled Neighbors"],"prefix":"10.1142","volume":"31","author":[{"given":"Xiaoling","family":"Song","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 40044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 40044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuelei","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 40044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 40044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 40044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,9,22]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022400231","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T07:08:30Z","timestamp":1663916910000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213022400231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":0,"journal-issue":{"issue":"06","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1142\/S0218213022400231"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022400231","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9]]},"article-number":"2240023"}}