{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:51:29Z","timestamp":1782154289406,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["No. 624MS039"],"award-info":[{"award-number":["No. 624MS039"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62166016"],"award-info":[{"award-number":["No. 62166016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00521-026-12085-0","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T07:51:10Z","timestamp":1778053870000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Iterative deep PU learning combined with data features"],"prefix":"10.1007","volume":"38","author":[{"given":"Hongnan","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaozhi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenguang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"issue":"1","key":"12085_CR1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.tcs.2005.09.007","volume":"348","author":"F Denis","year":"2005","unstructured":"Denis F, Gilleron R, Letouzey F (2005) Learning from positive and unlabeled examples. Theor Comput Sci 348(1):70\u201383","journal-title":"Theor Comput Sci"},{"issue":"1","key":"12085_CR2","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1186\/1471-2105-12-389","volume":"12","author":"F Mordelet","year":"2011","unstructured":"Mordelet F, Vert J-P (2011) Prodige: Prioritization of disease genes with multitask machine learning from positive and unlabeled examples. BMC Bioinformatics 12(1):389","journal-title":"BMC Bioinformatics"},{"key":"12085_CR3","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.patrec.2013.06.010","volume":"37","author":"F Mordelet","year":"2014","unstructured":"Mordelet F, Vert J-P (2014) A bagging svm to learn from positive and unlabeled examples. Pattern Recognit Lett 37:201\u2013209","journal-title":"Pattern Recognit Lett"},{"key":"12085_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2025.3600374","author":"C Yu","year":"2025","unstructured":"Yu C, Cheng H, Huang Y, Lin Z, Zhou T (2025) Multi-scale cross-dimensional attention network for gland segmentation. IEEE Signal Process Lett. https:\/\/doi.org\/10.1109\/lsp.2025.3600374","journal-title":"IEEE Signal Process Lett"},{"issue":"4","key":"12085_CR5","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s10994-020-05877-5","volume":"109","author":"J Bekker","year":"2020","unstructured":"Bekker J, Davis J (2020) Learning from positive and unlabeled data: a survey. Mach Learn 109(4):719\u2013760. https:\/\/doi.org\/10.1007\/s10994-020-05877-5","journal-title":"Mach Learn"},{"key":"12085_CR6","doi-asserted-by":"crossref","unstructured":"Du Plessis MC, Niu G, Sugiyama M (2014) Analysis of learning from positive and unlabeled data. Adv Neural Inf Process Syst 27","DOI":"10.1587\/transinf.E97.D.1358"},{"key":"12085_CR7","unstructured":"Du Plessis M, Niu G, Sugiyama M (2015) Convex formulation for learning from positive and unlabeled data. In: Int Conf Mach Learn, pp 1386\u20131394. http:\/\/proceedings.mlr.press\/v37\/plessis15.pdf . PMLR"},{"key":"12085_CR8","doi-asserted-by":"publisher","unstructured":"Kiryo R, Niu G, Du Plessis MC, Sugiyama M (2017) Positive-unlabeled learning with non-negative risk estimator. Adv Neural Inf Process Syst 30 https:\/\/doi.org\/10.48550\/arxiv.1703.00593","DOI":"10.48550\/arxiv.1703.00593"},{"key":"12085_CR9","doi-asserted-by":"publisher","unstructured":"Su G, Chen W, Xu M (2021) Positive-unlabeled learning from imbalanced data. In: IJCAI, pp 2995\u20133001. https:\/\/doi.org\/10.24963\/ijcai.2021\/412","DOI":"10.24963\/ijcai.2021\/412"},{"key":"12085_CR10","doi-asserted-by":"publisher","unstructured":"Chen X, Chen W, Chen T, Yuan Y, Gong C, Chen K, Wang Z (2020) Self-pu: Self boosted and calibrated positive-unlabeled training. In: Int Conf Mach Learn, pp 1510\u20131519. https:\/\/doi.org\/10.48550\/arxiv.2006.11280 . PMLR","DOI":"10.48550\/arxiv.2006.11280"},{"key":"12085_CR11","doi-asserted-by":"crossref","unstructured":"Zhu X, Goldberg A (2009) Introduction to Semi-supervised Learning. Morgan & Claypool Publishers","DOI":"10.1007\/978-3-031-01548-9"},{"key":"12085_CR12","unstructured":"Zhu XJ (2005) Semi-supervised learning literature survey"},{"key":"12085_CR13","doi-asserted-by":"publisher","unstructured":"He F, Liu T, Webb GI, Tao D (2018) Instance-dependent pu learning by bayesian optimal relabeling. https:\/\/doi.org\/10.48550\/arxiv.1808.02180","DOI":"10.48550\/arxiv.1808.02180"},{"key":"12085_CR14","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.neucom.2016.01.089","volume":"196","author":"D Ienco","year":"2016","unstructured":"Ienco D, Pensa RG (2016) Positive and unlabeled learning in categorical data. Neurocomputing 196:113\u2013124. https:\/\/doi.org\/10.1016\/j.neucom.2016.01.089","journal-title":"Neurocomputing"},{"issue":"5","key":"12085_CR15","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.6688\/jise.2014.30.5.10","volume":"30","author":"L Liu","year":"2014","unstructured":"Liu L, Peng T (2014) Clustering-based method for positive and unlabeled text categorization enhanced by improved tfidf. J Inf Sci Eng 30(5):1463\u20131481. https:\/\/doi.org\/10.6688\/jise.2014.30.5.10","journal-title":"J Inf Sci Eng"},{"key":"12085_CR16","doi-asserted-by":"publisher","unstructured":"Hsieh Y-G, Niu G, Sugiyama M (2019) Classification from positive, unlabeled and biased negative data. In: Int Conf Mach Learn, pp 2820\u20132829. https:\/\/doi.org\/10.48550\/arxiv.1810.00846 . PMLR","DOI":"10.48550\/arxiv.1810.00846"},{"key":"12085_CR17","doi-asserted-by":"crossref","unstructured":"Luo C, Zhao P, Chen C, Qiao B, Du C, Zhang H, Wu W, Cai S, He B, Rajmohan S et al (2021) Pulns: Positive-unlabeled learning with effective negative sample selector. In: Proc AAAI Conf Artif Intell 35:8784\u20138792","DOI":"10.1609\/aaai.v35i10.17064"},{"key":"12085_CR18","doi-asserted-by":"crossref","unstructured":"Chiaroni F, Rahal M-C, Hueber N, Dufaux F (2018) Learning with a generative adversarial network from a positive unlabeled dataset for image classification. In: 2018 25th Ieee Int Conf Image Proc (icip), pp 1368\u20131372. IEEE","DOI":"10.1109\/ICIP.2018.8451831"},{"key":"12085_CR19","doi-asserted-by":"crossref","unstructured":"Hou M, Chaib-Draa B, Li C, Zhao Q (2017) Generative adversarial positive-unlabelled learning. arXiv preprint arXiv:1711.08054","DOI":"10.24963\/ijcai.2018\/312"},{"key":"12085_CR20","doi-asserted-by":"publisher","unstructured":"Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 213\u2013220. https:\/\/doi.org\/10.1145\/1401890.1401920","DOI":"10.1145\/1401890.1401920"},{"key":"12085_CR21","doi-asserted-by":"publisher","unstructured":"Christoffel M, Niu G, Sugiyama M (2016) Class-prior estimation for learning from positive and unlabeled data. In: Asian Conf Mach Learn, pp 221\u2013236. https:\/\/doi.org\/10.1007\/s10994-016-5604-6 . PMLR","DOI":"10.1007\/s10994-016-5604-6"},{"key":"12085_CR22","unstructured":"Kato M, Teshima T, Honda J (2019) Learning from positive and unlabeled data with a selection bias. In: Int Conf Learn Repres. https:\/\/openreview.net\/pdf?id=rJzLciCqKm"},{"key":"12085_CR23","doi-asserted-by":"publisher","first-page":"13088","DOI":"10.48550\/arxiv.2002.10261","volume":"33","author":"Z Hammoudeh","year":"2020","unstructured":"Hammoudeh Z, Lowd D (2020) Learning from positive and unlabeled data with arbitrary positive shift. Adv Neural Inf Process Syst 33:13088\u201313099. https:\/\/doi.org\/10.48550\/arxiv.2002.10261","journal-title":"Adv Neural Inf Process Syst"},{"key":"12085_CR24","doi-asserted-by":"publisher","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural Inf Proc Syst 30. https:\/\/doi.org\/10.48550\/arxiv.1703.01780","DOI":"10.48550\/arxiv.1703.01780"},{"key":"12085_CR25","doi-asserted-by":"publisher","unstructured":"Luo Y, Zhu J, Li M, Ren Y, Zhang B (2018) Smooth neighbors on teacher graphs for semi-supervised learning. In: Proc IEEE Conf Comput Vision and Pattern Recog, pp 8896\u20138905. https:\/\/doi.org\/10.48550\/arxiv.1711.00258","DOI":"10.48550\/arxiv.1711.00258"},{"key":"12085_CR26","doi-asserted-by":"publisher","unstructured":"Xie Q, Dai Z, Hovy E, Luong T, Le Q (2020) Unsupervised data augmentation for consistency training. Adv Neural Inf Process Syst 33:6256\u20136268. https:\/\/doi.org\/10.48550\/arxiv.1904.12848","DOI":"10.48550\/arxiv.1904.12848"},{"key":"12085_CR27","doi-asserted-by":"publisher","unstructured":"Bromley J, Guyon I, LeCun Y, S\u00e4ckinger E, Shah R (1993) Signature verification using a\" siamese\" time delay neural network. Adv Neural Inf Proc Syst 6. https:\/\/doi.org\/10.1016\/j.patrec.2007.02.016","DOI":"10.1016\/j.patrec.2007.02.016"},{"key":"12085_CR28","doi-asserted-by":"publisher","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Comput Soc Conf Comput Vision and Pattern Recog (CVPR\u201906), 2:1735\u20131742. https:\/\/doi.org\/10.1109\/cvpr.2006.100 . IEEE","DOI":"10.1109\/cvpr.2006.100"},{"key":"12085_CR29","doi-asserted-by":"publisher","unstructured":"Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Comput Soc Conf Comput Vision and Pattern Recog (CVPR\u201905), 1:539\u2013546. https:\/\/doi.org\/10.1109\/cvpr.2005.202 . IEEE","DOI":"10.1109\/cvpr.2005.202"},{"key":"12085_CR30","doi-asserted-by":"publisher","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: Proc IEEE Conf Comput Vision and Pattern Recog, pp 1701\u20131708. https:\/\/doi.org\/10.1109\/cvpr.2014.220","DOI":"10.1109\/cvpr.2014.220"},{"issue":"11","key":"12085_CR31","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"12085_CR32","doi-asserted-by":"publisher","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. https:\/\/doi.org\/10.48550\/arxiv.1708.07747","DOI":"10.48550\/arxiv.1708.07747"},{"key":"12085_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.10.012","author":"A Krizhevsky","year":"2009","unstructured":"Krizhevsky A, Hinton G et al (2009). Learning multiple layers of features from tiny images. https:\/\/doi.org\/10.1016\/j.eswa.2018.10.012","journal-title":"Learning multiple layers of features from tiny images"},{"key":"12085_CR34","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) Striving for simplicity: The all convolutional net. https:\/\/doi.org\/https:\/\/arxiv.org\/pdf\/1412.6806.pdf"},{"key":"12085_CR35","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE Conf Comput Vision and Pattern Recog, pp 770\u2013778. https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/cvpr.2016.90"},{"key":"12085_CR36","doi-asserted-by":"publisher","DOI":"10.1002\/cta.4485","author":"H Cheng","year":"2025","unstructured":"Cheng H, Yu C, Zhang C (2025) Segmentation of ic images in integrated circuit reverse engineering using efficientnet encoder based on u-net++ architecture. Int J Circuit Theory Appl. https:\/\/doi.org\/10.1002\/cta.4485","journal-title":"Int J Circuit Theory Appl"},{"key":"12085_CR37","doi-asserted-by":"publisher","first-page":"14844","DOI":"10.48550\/arxiv.1906.00642","volume":"33","author":"H Chen","year":"2020","unstructured":"Chen H, Liu F, Wang Y, Zhao L, Wu H (2020) A variational approach for learning from positive and unlabeled data. Adv Neural Inf Process Syst 33:14844\u201314854. https:\/\/doi.org\/10.48550\/arxiv.1906.00642","journal-title":"Adv Neural Inf Process Syst"},{"key":"12085_CR38","doi-asserted-by":"publisher","unstructured":"Kingma DP (2014) Adam: A method for stochastic optimization. https:\/\/doi.org\/10.48550\/arxiv.1412.6980","DOI":"10.48550\/arxiv.1412.6980"},{"key":"12085_CR39","unstructured":"Loshchilov I (2017) Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"12085_CR40","doi-asserted-by":"publisher","unstructured":"Zhu Z, Wang L, Zhao P, Du C, Zhang W, Dong H, Qiao B, Lin Q, Rajmohan S, Zhang D (2023) Robust positive-unlabeled learning via noise negative sample self-correction. In: Proc 29th ACM SIGKDD Conf Knowl Dis and Data Mining, pp 3663\u20133673. https:\/\/doi.org\/10.1145\/3580305.3599491","DOI":"10.1145\/3580305.3599491"},{"key":"12085_CR41","doi-asserted-by":"publisher","unstructured":"Berthelot D, Carlini N, Goodfellow I, Papernot N, Oliver A, Raffel CA (2019) Mixmatch: A holistic approach to semi-supervised learning. Adv Neural Inf Proc Syst 32, https:\/\/doi.org\/10.48550\/arxiv.1905.02249","DOI":"10.48550\/arxiv.1905.02249"},{"key":"12085_CR42","doi-asserted-by":"publisher","unstructured":"Berthelot D, Carlini N, Cubuk ED, Kurakin A, Sohn K, Zhang H, Raffel C (2019) Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring. https:\/\/doi.org\/10.48550\/arxiv.1911.09785","DOI":"10.48550\/arxiv.1911.09785"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12085-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-12085-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12085-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:23:35Z","timestamp":1782152615000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-12085-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":42,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["12085"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-12085-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.22541\/au.174565088.80073719\/v1","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"3 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"369"}}