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Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and\n            <jats:italic>l<\/jats:italic>\n            <jats:sub>1<\/jats:sub>\n            regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.\n          <\/jats:p>","DOI":"10.1145\/3342512","type":"journal-article","created":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T12:14:48Z","timestamp":1567685688000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2561-6496","authenticated-orcid":false,"given":"Ali","family":"Braytee","sequence":"first","affiliation":[{"name":"Advanced Analytics Institute, University of Technology Sydney, Ultimo, NSW, Australia"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Advanced Analytics Institute, University of Technology Sydney, Ultimo, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8864-0314","authenticated-orcid":false,"given":"Ali","family":"Anaissi","sequence":"additional","affiliation":[{"name":"School of IT, Faculty of Engineering and IT, The University of Sydney, Camperdown, NSW, Australia"}]},{"given":"Paul J.","family":"Kennedy","sequence":"additional","affiliation":[{"name":"Centre of Artificial Intelligence, University of Technology Sydney, Camperdown, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2019,9,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btk048"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems. 730--738","author":"Bhatia Kush","year":"2015"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46675-0_9"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2015974"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.08.091"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.07.019"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Joint Conference on Neural Networks (IJCNN\u201906)","author":"Chen Ken"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning (ICML\u201913)","author":"Dembczynski Krzysztof","year":"2013"},{"key":"e_1_2_1_11_1","volume-title":"Statistical comparisons of classifiers over multiple data sets. 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