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Meanwhile, to enhance the explainable of neural network, Gaussian process is adopted to simulate the real underlying distribution of multi-label dataset. Besides, this paper also considers that contributions of different features have different effects on the experimental results, thus self-attention is leveraged to balance various features. Finally, the applicability of the algorithm is verified in three sparse datasets, and the generalization performance is also validated in three groups of benchmark datasets. <\/jats:p>","DOI":"10.1142\/s0218488523500046","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T10:06:39Z","timestamp":1677665199000},"page":"25-55","source":"Crossref","is-referenced-by-count":1,"title":["Abstracting Instance Information and Inter-Label Relations for Sparse Multi-Label Classification"],"prefix":"10.1142","volume":"31","author":[{"given":"Si-Ming","family":"Lian","sequence":"first","affiliation":[{"name":"Beijing Institute of Aerospace Control Devices, Beijing 100039, P. R. China"},{"name":"Department of Automation, College of Information Science and Engineering, China University of Petroleum Beijing, Beijing, P. R. 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