{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T20:23:34Z","timestamp":1720729414750},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Swarm and Evolutionary Computation"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1016\/j.swevo.2022.101174","type":"journal-article","created":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T15:37:13Z","timestamp":1662219433000},"page":"101174","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["A loss matrix-based alternating optimization method for sparse PU learning"],"prefix":"10.1016","volume":"75","author":[{"given":"Jianfeng","family":"Qiu","sequence":"first","affiliation":[]},{"given":"Xiaoqiang","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.swevo.2022.101174_b1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.ins.2017.12.046","article-title":"Positive unlabeled learning for building recommender systems in a parliamentary setting","volume":"433","author":"de Campos","year":"2018","journal-title":"Inf. Sci."},{"key":"10.1016\/j.swevo.2022.101174_b2","unstructured":"Mariana\u00a0Caravanti de\u00a0Souza, Bruno\u00a0Magalhaes Nogueira, Rafael\u00a0Geraldeli Rossi, Ricardo\u00a0Marcondes Marcacini, Brucce\u00a0Neves dos Santos, Solange\u00a0Oliveira Rezende, A network-based positive and unlabeled learning approach for fake news detection, Mach. Learn. 1\u201344."},{"key":"10.1016\/j.swevo.2022.101174_b3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ymeth.2020.05.007","article-title":"Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning","volume":"179","author":"Zhang","year":"2020","journal-title":"Methods"},{"issue":"212","key":"10.1016\/j.swevo.2022.101174_b4","first-page":"1","article-title":"Age is important for the early-stage detection of breast cancer on both transcriptomic and methylomic biomarkers","volume":"10","author":"Feng","year":"2019","journal-title":"Front. Genet."},{"issue":"4","key":"10.1016\/j.swevo.2022.101174_b5","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.swevo.2022.101174_b6","series-title":"International Conference on Knowledge Discovery and Data Mining","first-page":"239","article-title":"PEBL: positive example based learning for web page classification using SVM","author":"Yu","year":"2002"},{"key":"10.1016\/j.swevo.2022.101174_b7","series-title":"International Joint Conference on Artificial Intelligence, Vol. 3","first-page":"587","article-title":"Learning to classify texts using positive and unlabeled data","author":"Li","year":"2003"},{"key":"10.1016\/j.swevo.2022.101174_b8","series-title":"International Conference on Data Mining, Vol. 3","first-page":"179","article-title":"Building text classifiers using positive and unlabeled examples","author":"Liu","year":"2003"},{"key":"10.1016\/j.swevo.2022.101174_b9","series-title":"International Conference on Artificial Intelligence","first-page":"8784","article-title":"PULNS: positive-unlabeled learning with effective negative sample selector","author":"Luo","year":"2021"},{"key":"10.1016\/j.swevo.2022.101174_b10","series-title":"International Conference on Knowledge Discovery and Data Mining","first-page":"213","article-title":"Learning classifiers from only positive and unlabeled data","author":"Elkan","year":"2008"},{"key":"10.1016\/j.swevo.2022.101174_b11","series-title":"International Joint Conference on Artificial Intelligence","first-page":"2689","article-title":"Positive and unlabeled learning via loss decomposition and centroid estimation","author":"Shi","year":"2018"},{"key":"10.1016\/j.swevo.2022.101174_b12","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/TPAMI.2019.2941684","article-title":"Loss decomposition and centroid estimation for positive and unlabeled learning","volume":"43","author":"Gong","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.swevo.2022.101174_b13","series-title":"Confident learning: Estimating uncertainty in dataset labels","author":"Northcutt","year":"2019"},{"key":"10.1016\/j.swevo.2022.101174_b14","series-title":"International Conference on Machine Learning","first-page":"1386","article-title":"Convex formulation for learning from positive and unlabeled data","author":"Du\u00a0Plessis","year":"2015"},{"key":"10.1016\/j.swevo.2022.101174_b15","series-title":"Advances in Neural Information Processing Systems","first-page":"1675","article-title":"Positive-unlabeled learning with non-negative risk estimator","author":"Kiryo","year":"2017"},{"key":"10.1016\/j.swevo.2022.101174_b16","series-title":"International Conference on Machine Learning","first-page":"2820","article-title":"Classification from positive, unlabeled and biased negative data","author":"Hsieh","year":"2019"},{"key":"10.1016\/j.swevo.2022.101174_b17","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."},{"issue":"19","key":"10.1016\/j.swevo.2022.101174_b18","first-page":"1","article-title":"DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions","volume":"20","author":"Zheng","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.swevo.2022.101174_b19","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compbiolchem.2018.05.022","article-title":"C-PUGP: A cluster-based positive unlabeled learning method for disease gene prediction and prioritization","volume":"76","author":"Vasighizaker","year":"2018","journal-title":"Comput. Biol. Chem."},{"issue":"5","key":"10.1016\/j.swevo.2022.101174_b20","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/TMM.2018.2871421","article-title":"Boosting positive and unlabeled learning for anomaly detection with multi-features","volume":"21","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Multimed."},{"issue":"20","key":"10.1016\/j.swevo.2022.101174_b21","doi-asserted-by":"crossref","first-page":"2640","DOI":"10.1093\/bioinformatics\/bts504","article-title":"Positive-unlabeled learning for disease gene identification","volume":"28","author":"Yang","year":"2012","journal-title":"Bioinformatics"},{"issue":"12","key":"10.1016\/j.swevo.2022.101174_b22","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nature Biotechnol."},{"key":"10.1016\/j.swevo.2022.101174_b23","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.ins.2021.08.099","article-title":"A graph-based approach for positive and unlabeled learning","volume":"580","author":"Carnevali","year":"2021","journal-title":"Inform. Sci."},{"issue":"11","key":"10.1016\/j.swevo.2022.101174_b24","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.swevo.2022.101174_b25","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2021.3061456","article-title":"Instance-dependent positive and unlabeled learning with labeling bias estimation","author":"Gong","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.swevo.2022.101174_b26","article-title":"AdaBoost-based transfer learning method for positive and unlabelled learning problem","author":"Liu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.swevo.2022.101174_b27","unstructured":"Wee\u00a0Sun Lee, Bing Liu, Learning with positive and unlabeled examples using weighted logistic regression, in: International Conference on Machine Learning."},{"key":"10.1016\/j.swevo.2022.101174_b28","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1613\/jair.1.12125","article-title":"Confident learning: Estimating uncertainty in dataset labels","volume":"70","author":"Northcutt","year":"2021","journal-title":"J. Artificial Intelligence Res."},{"issue":"5","key":"10.1016\/j.swevo.2022.101174_b29","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/TCYB.2018.2816984","article-title":"AdaSampling for positive-unlabeled and label noise learning with bioinformatics applications","volume":"49","author":"Yang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.swevo.2022.101174_b30","series-title":"Advances in Neural Information Processing Systems","first-page":"703","article-title":"Analysis of learning from positive and unlabeled data","author":"Du\u00a0Plessis","year":"2014"},{"key":"10.1016\/j.swevo.2022.101174_b31","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":"Inform. Sci."},{"key":"10.1016\/j.swevo.2022.101174_b32","series-title":"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.swevo.2022.101174_b33","series-title":"International Conference on Machine Learning, Vol. 70","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","author":"Sakai","year":"2017"},{"key":"10.1016\/j.swevo.2022.101174_b34","doi-asserted-by":"crossref","unstructured":"Jessa Bekker, Jesse Davis, Estimating the class prior in positive and unlabeled data through decision tree induction, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 2018.","DOI":"10.1609\/aaai.v32i1.11715"},{"key":"10.1016\/j.swevo.2022.101174_b35","doi-asserted-by":"crossref","unstructured":"Shizhen Chang, Bo Du, Liangpei Zhang, Positive unlabeled learning with class-prior approximation, in: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on ArtificialIntelligence, 2021, pp. 2014\u20132021.","DOI":"10.24963\/ijcai.2020\/279"},{"issue":"6","key":"10.1016\/j.swevo.2022.101174_b36","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","article-title":"Particle swarm optimization for feature selection in classification: a multi-objective approach","volume":"43","author":"Xue","year":"2013","journal-title":"IEEE Trans. Cybern."},{"issue":"2","key":"10.1016\/j.swevo.2022.101174_b37","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"10.1016\/j.swevo.2022.101174_b38","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.knosys.2019.01.029","article-title":"Multi-objective evolutionary algorithm for optimizing the partial area under the ROC curve","volume":"170","author":"Cheng","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.swevo.2022.101174_b39","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ins.2020.03.032","article-title":"Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection","volume":"523","author":"Li","year":"2020","journal-title":"Inf. Sci."},{"issue":"2","key":"10.1016\/j.swevo.2022.101174_b40","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."},{"key":"10.1016\/j.swevo.2022.101174_b41","series-title":"Multi-Objective Evolutionary Optimisation for Product Design and Manufacturing","first-page":"3","article-title":"Multi-objective optimisation using evolutionary algorithms: an introduction","author":"Deb","year":"2011"},{"key":"10.1016\/j.swevo.2022.101174_b42","first-page":"30","article-title":"A combined genetic adaptive search (GeneAS) for engineering design","volume":"26","author":"Deb","year":"1996","journal-title":"Comput. Sci. Inf."},{"key":"10.1016\/j.swevo.2022.101174_b43","doi-asserted-by":"crossref","unstructured":"Chuang Zhang, Chen Gong, Tengfei Liu, Xun Lu, Weiqiang Wang, Jian Yang, Online positive and unlabeled learning, in: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on ArtificialIntelligence, 2021, pp. 2248\u20132254.","DOI":"10.24963\/ijcai.2020\/311"},{"issue":"10","key":"10.1016\/j.swevo.2022.101174_b44","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":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.swevo.2022.101174_b45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2017.06.002","article-title":"A survey on multi-objective evolutionary algorithms for the solution of the environmental\/economic dispatch problems","volume":"38","author":"Qu","year":"2018","journal-title":"Swarm Evol. Comput."}],"container-title":["Swarm and Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2210650222001419?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2210650222001419?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T17:41:46Z","timestamp":1706722906000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2210650222001419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":45,"alternative-id":["S2210650222001419"],"URL":"https:\/\/doi.org\/10.1016\/j.swevo.2022.101174","relation":{},"ISSN":["2210-6502"],"issn-type":[{"value":"2210-6502","type":"print"}],"subject":[],"published":{"date-parts":[[2022,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A loss matrix-based alternating optimization method for sparse PU learning","name":"articletitle","label":"Article Title"},{"value":"Swarm and Evolutionary Computation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.swevo.2022.101174","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101174"}}