{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T14:27:46Z","timestamp":1787927266142,"version":"build-2784847793"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12205003"],"award-info":[{"award-number":["12205003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62403001"],"award-info":[{"award-number":["62403001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076001"],"award-info":[{"award-number":["62076001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2308085MF201"],"award-info":[{"award-number":["2308085MF201"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2408085QF198"],"award-info":[{"award-number":["2408085QF198"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2408085MF152"],"award-info":[{"award-number":["2408085MF152"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ins.2026.123739","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:21:22Z","timestamp":1780935682000},"page":"123739","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A similarity-guided evolutionary multitasking approach for high-dimensional positive-unlabeled learning"],"prefix":"10.1016","volume":"755","author":[{"given":"Jianfeng","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0479-2378","authenticated-orcid":false,"given":"Meiwen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4539-0335","authenticated-orcid":false,"given":"Kaixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6447-2053","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0175-0818","authenticated-orcid":false,"given":"Fan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123739_bib0005","doi-asserted-by":"crossref","first-page":"90287","DOI":"10.1109\/ACCESS.2024.3420453","article-title":"Dense-PU: learning a density-based boundary for positive and unlabeled learning","volume":"12","author":"Sevetlidis","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2026.123739_bib0010","series-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","first-page":"2747","article-title":"Community-based hierarchical positive-unlabeled (PU) model fusion for chronic disease prediction","author":"Wu","year":"2023"},{"issue":"4","key":"10.1016\/j.ins.2026.123739_bib0015","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.ipm.2014.11.001","article-title":"Detecting positive and negative deceptive opinions using PU-learning","volume":"51","author":"Fusilier","year":"2015","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.ins.2026.123739_bib0020","series-title":"Proceedings of the 18th ACM Conference on Recommender Systems","first-page":"247","article-title":"Unlocking the hidden treasures: enhancing recommendations with unlabeled data","author":"Zhao","year":"2024"},{"issue":"3","key":"10.1016\/j.ins.2026.123739_bib0025","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/JBHI.2024.3349412","article-title":"Semi-supervised disease classification based on limited medical image data","volume":"28","author":"Zhang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.ins.2026.123739_bib0030","series-title":"Proceedings of the International Conference on Machine Learning, 2","first-page":"387","article-title":"Partially supervised classification of text documents","author":"Liu","year":"2002"},{"key":"10.1016\/j.ins.2026.123739_bib0035","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence, 3","first-page":"587","article-title":"Learning to classify texts using positive and unlabeled data","author":"Li","year":"2003"},{"key":"10.1016\/j.ins.2026.123739_bib0040","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.patrec.2017.03.007","article-title":"Bayesian belief network for positive unlabeled learning with uncertainty","volume":"90","author":"Gan","year":"2017","journal-title":"Pattern Recognit. Lett."},{"issue":"10","key":"10.1016\/j.ins.2026.123739_bib0045","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1109\/TNNLS.2018.2870666","article-title":"A robust AUC maximization framework with simultaneous outlier detection and feature selection for positive-unlabeled classification","volume":"30","author":"Ren","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"10.1016\/j.ins.2026.123739_bib0050","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s10994-020-05877-5","article-title":"Learning from positive and unlabeled data: a survey","volume":"109","author":"Bekker","year":"2020","journal-title":"Mach. Learn."},{"key":"10.1016\/j.ins.2026.123739_bib0055","series-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","first-page":"4250","article-title":"Positive and unlabeled learning with label disambiguation","author":"Zhang","year":"2019"},{"key":"10.1016\/j.ins.2026.123739_bib0060","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.ins.2021.01.002","article-title":"Cost-sensitive positive and unlabeled learning","volume":"558","author":"Chen","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123739_bib0065","series-title":"Advances in Neural Information Processing Systems, 35","first-page":"24060","article-title":"Positive-unlabeled learning using random forests via recursive greedy risk minimization","author":"Wilton","year":"2022"},{"key":"10.1016\/j.ins.2026.123739_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106986","article-title":"An evolutionary multi-objective approach to learn from positive and unlabeled data","volume":"101","author":"Qiu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.ins.2026.123739_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2022.101174","article-title":"A loss matrix-based alternating optimization method for sparse PU learning","volume":"75","author":"Qiu","year":"2022","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.ins.2026.123739_bib0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120992","article-title":"A multi-objective evolutionary algorithm for robust positive-unlabeled learning","volume":"678","author":"Qiu","year":"2024","journal-title":"Inf. Sci."},{"issue":"4","key":"10.1016\/j.ins.2026.123739_bib0085","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1049\/cit2.12152","article-title":"A novel observation points-based positive-unlabeled learning algorithm","volume":"8","author":"He","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.ins.2026.123739_bib0090","series-title":"Third IEEE International Conference on Data Mining","first-page":"179","article-title":"Building text classifiers using positive and unlabeled examples","author":"Liu","year":"2003"},{"key":"10.1016\/j.ins.2026.123739_bib0095","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","author":"Sakai","year":"2017"},{"issue":"2","key":"10.1016\/j.ins.2026.123739_bib0100","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s12559-016-9395-7","article-title":"Evolutionary multitasking: a computer science view of cognitive multitasking","volume":"8","author":"Ong","year":"2016","journal-title":"Cogn. Comput."},{"issue":"3","key":"10.1016\/j.ins.2026.123739_bib0105","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/TEVC.2015.2458037","article-title":"Multifactorial evolution: toward evolutionary multitasking","volume":"20","author":"Gupta","year":"2015","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"9","key":"10.1016\/j.ins.2026.123739_bib0110","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1109\/TCYB.2018.2845361","article-title":"Evolutionary multitasking via explicit autoencoding","volume":"49","author":"Feng","year":"2018","journal-title":"IEEE Trans. Cybern."},{"issue":"2","key":"10.1016\/j.ins.2026.123739_bib0115","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1109\/TEVC.2021.3068157","article-title":"Solving multitask optimization problems with adaptive knowledge transfer via anomaly detection","volume":"26","author":"Wang","year":"2021","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.ins.2026.123739_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114076","article-title":"Enhancing evolutionary multitasking for high-dimensional feature selection through task relevance evaluation and knowledge transfer","author":"Yu","year":"2025","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.ins.2026.123739_bib0125","article-title":"An evolutionary multitasking method for positive and unlabeled learning","volume":"331","author":"Tang","year":"2025","journal-title":"Knowl.-based Syst."},{"issue":"5","key":"10.1016\/j.ins.2026.123739_bib0130","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1109\/TCYB.2018.2883082","article-title":"Selection of robust and relevant features for 3-D steganalysis","volume":"50","author":"Li","year":"2018","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"10.1016\/j.ins.2026.123739_bib0135","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1109\/TIT.2014.2320500","article-title":"R\u00e9nyi divergence and Kullback-Leibler divergence","volume":"60","author":"Van Erven","year":"2014","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"2","key":"10.1016\/j.ins.2026.123739_bib0140","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"11","key":"10.1016\/j.ins.2026.123739_bib0145","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.1109\/TNNLS.2019.2892403","article-title":"Large-margin label-calibrated support vector machines for positive and unlabeled learning","volume":"30","author":"Gong","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.ins.2026.123739_bib0150","series-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence","first-page":"2248","article-title":"Online positive and unlabeled learning","author":"Zhang","year":"2021"},{"key":"10.1016\/j.ins.2026.123739_bib0155","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.cor.2016.06.021","article-title":"A box decomposition algorithm to compute the hypervolume indicator","volume":"79","author":"Lacour","year":"2017","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.ins.2026.123739_bib0160","first-page":"1","article-title":"ML-AMPSIT: machine learning-based automated multi-method parameter sensitivity and importance analysis tool","author":"Di Santo","year":"2024","journal-title":"Geosci. Model Dev."},{"key":"10.1016\/j.ins.2026.123739_bib0165","series-title":"The 16th Asian Conference on Machine Learning, 260","first-page":"33","article-title":"A novel evolutionary multitasking feature selection approach for genomic data classification","author":"Yifan","year":"2024"},{"key":"10.1016\/j.ins.2026.123739_bib0170","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TETCI.2024.3397996","article-title":"A generative adversarial networks model based evolutionary algorithm for multimodal multi-objective optimization","author":"Dang","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"issue":"4","key":"10.1016\/j.ins.2026.123739_bib0175","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MCI.2025.3580520","article-title":"Evaluation of large language models as solution generators in complex optimization","volume":"20","author":"Huang","year":"2025","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.ins.2026.123739_bib0180","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence, 39","first-page":"23000","article-title":"Design principle transfer in neural architecture search via large language models","author":"Zhou","year":"2025"},{"issue":"7","key":"10.1016\/j.ins.2026.123739_bib0185","doi-asserted-by":"crossref","first-page":"3342","DOI":"10.1109\/TCYB.2025.3561518","article-title":"Learning to transfer for evolutionary multitasking","volume":"55","author":"Wu","year":"2025","journal-title":"IEEE Trans. Cybern."}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526006705?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526006705?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:44:45Z","timestamp":1787924685000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526006705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":37,"alternative-id":["S0020025526006705"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123739","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A similarity-guided evolutionary multitasking approach for high-dimensional positive-unlabeled learning","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123739","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123739"}}