Two‐stage‐neighborhood‐based multilabel classification for incomplete data with missing labels

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Two-stage-neighborhood-based multilabel classification for incomplete data with missing labels Lin Sun, Corresponding Author Lin Sun sunlin@htu.edu.cn orcid.org/0000-0003-4917-7651 College of Computer and Information Engineering, Henan Normal University, Xinxiang, China Engineering Laboratory of Intelligence Business and Internet of Things Technology, Henan Normal University, Xinxiang, China Correspondence Lin Sun and Tianxiang Wang, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China Email: sunlin@htu.edu.cn and wtx0719@126.com Weiping Ding, School of Information Science and Technology, Nantong University, 226019 Nantong, China Email: dwp9988@163.comSearch for more papers by this authorTianxiang Wang, Corresponding Author Tianxiang Wang wtx0719@126.com orcid.org/0000-0002-7779-1678 College of Computer and Information Engineering, Henan Normal University, Xinxiang, China Correspondence Lin Sun and Tianxiang Wang, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China Email: sunlin@htu.edu.cn and wtx0719@126.com Weiping Ding, School of Information Science and Technology, Nantong University, 226019 Nantong, China Email: dwp9988@163.comSearch for more papers by this authorWeiping Ding, Corresponding Author Weiping Ding dwp9988@163.com orcid.org/0000-0002-3180-7347 School of Information Science and Technology, Nantong University, Nantong, China Correspondence Lin Sun and Tianxiang Wang, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China Email: sunlin@htu.edu.cn and wtx0719@126.com Weiping Ding, School of Information Science and Technology, Nantong University, 226019 Nantong, China Email: dwp9988@163.comSearch for more papers by this authorJiucheng Xu, Jiucheng Xu orcid.org/0000-0003-1518-3623 College of Computer and Information Engineering, Henan Normal University, Xinxiang, ChinaSearch for more papers by this authorAnhui Tan, Anhui Tan orcid.org/0000-0002-2525-3499 School of Mathematics, Physics, and Information Science, Zhejiang Ocean University, Zhoushan, ChinaSearch for more papers by this author Lin Sun, Corresponding Author Lin Sun sunlin@htu.edu.cn orcid.org/0000-0003-4917-7651 College of Computer and Information Engineering, Henan Normal University, Xinxiang, China Engineering Laboratory of Intelligence Business and Internet of Things Technology, Henan Normal University, Xinxiang, China Correspondence Lin Sun and Tianxiang Wang, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China Email: sunlin@htu.edu.cn and wtx0719@126.com Weiping Ding, School of Information Science and Technology, Nantong University, 226019 Nantong, China Email: dwp9988@163.comSearch for more papers by this authorTianxiang Wang, Corresponding Author Tianxiang Wang wtx0719@126.com orcid.org/0000-0002-7779-1678 College of Computer and Information Engineering, Henan Normal University, Xinxiang, China Correspondence Lin Sun and Tianxiang Wang, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China Email: sunlin@htu.edu.cn and wtx0719@126.com Weiping Ding, School of Information Science and Technology, Nantong University, 226019 Nantong, China Email: dwp9988@163.comSearch for more papers by this authorWeiping Ding, Corresponding Author Weiping Ding dwp9988@163.com orcid.org/0000-0002-3180-7347 School of Information Science and Technology, Nantong University, Nantong, China Correspondence Lin Sun and Tianxiang Wang, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China Email: sunlin@htu.edu.cn and wtx0719@126.com Weiping Ding, School of Information Science and Technology, Nantong University, 226019 Nantong, China Email: dwp9988@163.comSearch for more papers by this authorJiucheng Xu, Jiucheng Xu orcid.org/0000-0003-1518-3623 College of Computer and Information Engineering, Henan Normal University, Xinxiang, ChinaSearch for more papers by this authorAnhui Tan, Anhui Tan orcid.org/0000-0002-2525-3499 School of Mathematics, Physics, and Information Science, Zhejiang Ocean University, Zhoushan, ChinaSearch for more papers by this author First published: 01 March 2022 https://doi.org/10.1002/int.22861Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract In recent years, it has been difficult for multilabel classification to obtain complete multilabel data in real-world applications, and even a large number of labels for training samples are randomly missed. As a result, the classification task of incomplete multilabel data with missing labels faces formidable challenges. This paper presents a two-stage-neighborhood-based multilabel classification method for incomplete data with missing labels in neighborhood decision systems. First, to solve the problem of selecting the neighborhood radius manually, as well as balancing the samples in the neighborhood, the neighborhood radius based on the feature distribution function is defined, and the differences and similarities between samples through the identifiable and indiscernible matrices are, respectively, computed. Then, a restoration method for missing feature values is proposed for use in the first stage. Second, to consider the nonlinear relationship among features, a neighborhood-based fuzzy similarity relationship between samples is investigated based on the Gaussian kernel function. By integrating the fuzzy similarity relationship matrix, label-specific feature matrix, and label correlation matrix, an objective function based on the regression model is presented, the optimal solutions to the label-specific feature and label correlation matrices based on the gradient descent strategy are provided, and a new multilabel classification method with missing labels is developed during the second stage. Finally, two-stage multilabel classification algorithms are designed. Experiments on 18 multilabel data sets demonstrate that our designed algorithms are effective not only for recovering missing feature values, but also for improving the classification performance of data with missing labels. 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