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The manual annotation of available datasets is time-consuming and need a huge effort from the expert, especially for Multi-label applications in which each example of learning is associated with many labels at once. To overcome the manual annotation drawback, and to take advantages from the large amounts of unlabeled data, many semi-supervised approaches were proposed in the literature to give more sophisticated and fast solutions to support the automatic labeling of the unlabeled data. In this paper, a Collaborative Bagged Multi-label K-Nearest-Neighbors (<jats:italic>CobMLKNN<\/jats:italic>) algorithm is proposed, that extend the<jats:italic>co-Training<\/jats:italic>paradigm by a Multi-label K-Nearest-Neighbors algorithm. Experiments on ten real-world Multi-label datasets show the effectiveness of<jats:italic>CobMLKNN<\/jats:italic>algorithm to improve the performance of<jats:italic>MLKNN<\/jats:italic>to learn from a small number of labeled samples by exploiting unlabeled samples.<\/jats:p>","DOI":"10.1515\/comp-2019-0017","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T07:40:44Z","timestamp":1571038844000},"page":"226-242","source":"Crossref","is-referenced-by-count":3,"title":["Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors"],"prefix":"10.1515","volume":"9","author":[{"given":"Nesma","family":"Settouti","sequence":"first","affiliation":[{"name":"Biomedical Engineering Laboratory , Faculty of Technology , Tlemcen University , 13000 Tlemcen , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalida","family":"Douibi","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory , Faculty of Technology , Tlemcen University , 13000 Tlemcen , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed El Amine","family":"Bechar","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory , Faculty of Technology , Tlemcen University , 13000 Tlemcen , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostafa El Habib","family":"Daho","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory , Faculty of Technology , Tlemcen University , 13000 Tlemcen , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meryem","family":"Saidi","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory , Faculty of Technology , Tlemcen University , 13000 Tlemcen , Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2019,10,11]]},"reference":[{"key":"2022042707443480458_j_comp-2019-0017_ref_001_w2aab3b7c16b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] Madjarov G., Kocev D., Gjorgjevikj D., Deroski S., An Extensive Experimental Comparison of Methods for Multi-label Learning, Pattern Recogn., 45(9), 2012, 3084\u20133104, DOI:10.1016\/j.patcog.2012.03.00410.1016\/j.patcog.2012.03.004","DOI":"10.1016\/j.patcog.2012.03.004"},{"key":"2022042707443480458_j_comp-2019-0017_ref_002_w2aab3b7c16b1b6b1ab1ab2Aa","unstructured":"[2] Zhang M.L., Zhou Z.H., ML-KNN: A Lazy Learning Approach to Multi-label Learning, Pattern Recogn., 40((7)), 2007, 2038\u20132048, DOI:10.1016\/j.patcog.2006.12.01910.1016\/j.patcog.2006.12.019"},{"key":"2022042707443480458_j_comp-2019-0017_ref_003_w2aab3b7c16b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] Blum A., Mitchell T., Combining labeled and unlabeled data with co-training, in Proceedings of the eleventh annual conference on Computational learning theory, COLT\u2019 98, New York, NY, USA, 1998, 92\u201310010.1145\/279943.279962","DOI":"10.1145\/279943.279962"},{"key":"2022042707443480458_j_comp-2019-0017_ref_004_w2aab3b7c16b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] Li M., Zhou Z.H., Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples, IEEE Transactions on Systems, Man, and Cybernetics - 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