{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:22:55Z","timestamp":1771413775063,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T00:00:00Z","timestamp":1560729600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T00:00:00Z","timestamp":1560729600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s11704-018-7182-1","type":"journal-article","created":{"date-parts":[[2018,12,29]],"date-time":"2018-12-29T02:20:19Z","timestamp":1546050019000},"page":"996-1009","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Transfer synthetic over-sampling for class-imbalance learning with limited minority class data"],"prefix":"10.1007","volume":"13","author":[{"given":"Xu-Ying","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng-Tao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min-Ling","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,17]]},"reference":[{"issue":"9","key":"7182_CR1","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia E A. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263\u20131284","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"7182_CR2","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","volume":"39","author":"X Y Liu","year":"2009","unstructured":"Liu X Y, Wu J, Zhou Z H. Exploratory undersampling for classimbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(2): 539\u2013550","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part^B (Cybernetics)"},{"key":"7182_CR3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-540-87479-9_34","volume-title":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"D Cieslak","year":"2008","unstructured":"Cieslak D, Chawla N. Learning decision trees for unbalanced data. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2008, 241\u2013256"},{"issue":"4","key":"7182_CR4","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(4): 463\u2013484","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part^C (Applications and Reviews)"},{"issue":"5","key":"7182_CR5","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2015","unstructured":"Wang S, Minku L L, Yao X. Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1356\u20131368","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"7182_CR6","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1109\/ISM.2015.126","volume-title":"Proceedings of the 2015^IEEE International Symposium on Multimedia","author":"Y Yan","year":"2015","unstructured":"Yan Y, Chen M, Shyu M L, Chen S C. Deep learning for imbalanced multimedia data classification. In: Proceedings of the 2015 IEEE International Symposium on Multimedia. 2015, 483\u2013488"},{"key":"7182_CR7","doi-asserted-by":"publisher","first-page":"4368","DOI":"10.1109\/IJCNN.2016.7727770","volume-title":"Proceedings of the 2016 International Joint Conference on Neural Networks","author":"S Wang","year":"2016","unstructured":"Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy P J. Training deep neural networks on imbalanced data sets. In: Proceedings of the 2016 International Joint Conference on Neural Networks. 2016, 4368\u20134374"},{"key":"7182_CR8","first-page":"8","volume-title":"Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining","author":"T Fawcett","year":"1996","unstructured":"Fawcett T, Provost F J. Combining data mining and machine learning for effective user profiling. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 1996, 8\u201313"},{"issue":"2\u20133","key":"7182_CR9","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1023\/A:1007452223027","volume":"30","author":"M Kubat","year":"1998","unstructured":"Kubat M, Holte R C, Matwin S. Machine learning for the detection of oil spills in satellite radar images. Machine Learning, 1998, 30(2\u20133): 195\u2013215","journal-title":"Machine Learning"},{"key":"7182_CR10","first-page":"81","volume-title":"Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval","author":"D D Lewis","year":"1994","unstructured":"Lewis D D, Ringuette M. A comparison of two learning algorithms for text categorization. In: Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval. 1994, 81\u201393"},{"issue":"2","key":"7182_CR11","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1109\/TR.2013.2259203","volume":"62","author":"S Wang","year":"2013","unstructured":"Wang S, Yao X. Using class imbalance learning for software defect prediction. IEEE Transactions on Reliability, 2013, 62(2): 434\u2013443","journal-title":"IEEE Transactions on Reliability"},{"issue":"6","key":"7182_CR12","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"A P Bradley","year":"1997","unstructured":"Bradley A P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30(6): 1145\u20131159","journal-title":"Pattern Recognition"},{"issue":"4","key":"7182_CR13","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1142\/S0219622006002258","volume":"5","author":"Q Yang","year":"2006","unstructured":"Yang Q, Wu X. 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making, 2006, 5(4): 597\u2013604","journal-title":"International Journal of Information Technology and Decision Making"},{"issue":"1","key":"7182_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1145\/1007730.1007734","volume":"6","author":"G M Weiss","year":"2004","unstructured":"Weiss G M. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 7\u201319","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"7182_CR15","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/0-387-25465-X_35","volume-title":"Data Mining and Knowledge Discovery Handbook, Springer, Boston, MA","author":"G M Weiss","year":"2005","unstructured":"Weiss G M. Mining with Rare Cases. Data Mining and Knowledge Discovery Handbook, Springer, Boston, MA. 2005, 765\u2013776"},{"key":"7182_CR16","first-page":"348","volume-title":"Proceedings of the 6th International Conference on Machine Learning and Applications","author":"T M Khoshgoftaar","year":"2007","unstructured":"Khoshgoftaar T M, Seiffert C, Hulse J V, Napolitano A, Folleco A. Learning with limited minority class data. In: Proceedings of the 6th International Conference on Machine Learning and Applications. 2007, 348\u2013353"},{"key":"7182_CR17","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N V Chawla","year":"2002","unstructured":"Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16: 321\u2013357","journal-title":"Journal of Artificial Intelligence Research"},{"key":"7182_CR18","first-page":"878","volume-title":"Proceedings of the International Conference on Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han H, Wang W Y, Mao B H. Borderline-SMOTE: a new oversampling method in imbalanced data sets learning. In: Proceedings of the International Conference on Intelligent Computing. 2005, 878\u2013887"},{"issue":"1","key":"7182_CR19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"G E Batista","year":"2004","unstructured":"Batista G E, Prati R C, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM SGKDD Explorations Newsletter, 2004, 6(1): 20\u201329","journal-title":"ACM SGKDD Explorations Newsletter"},{"key":"7182_CR20","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/3-540-48229-6_9","volume-title":"Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe","author":"J Laurikkala","year":"2001","unstructured":"Laurikkala J. Improving identification of difficult small classes by balancing class distribution. In: Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe. 2001, 63\u201366"},{"key":"7182_CR21","first-page":"1322","volume-title":"Proceedings of the 2008^IEEE International Joint Conference on Neural Networks","author":"H He","year":"2008","unstructured":"He H, Bai Y, Garcia E A, Li S. ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks. 2008, 1322\u20131328"},{"key":"7182_CR22","first-page":"111","volume-title":"Proceedings of the 13th^IEEE International Conference on Data Mining","author":"B Das","year":"2013","unstructured":"Das B, Krishnan N C, Cook D J. wRACOG: a gibbs sampling-based oversampling technique. In: Proceedings of the 13th IEEE International Conference on Data Mining. 2013, 111\u2013120"},{"key":"7182_CR23","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2013.12.003","volume":"20","author":"H Zhang","year":"2014","unstructured":"Zhang H, Li M. RWO-sampling: a random walk over-sampling approach to imbalanced data classification. Information Fusion, 2014, 20: 99\u2013116","journal-title":"Information Fusion"},{"issue":"10","key":"7182_CR24","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"S J Pan","year":"2010","unstructured":"Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345\u20131359","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"12","key":"7182_CR25","doi-asserted-by":"publisher","first-page":"3460","DOI":"10.1016\/j.patcog.2013.05.006","volume":"46","author":"M Galar","year":"2013","unstructured":"Galar M, Fern\u00e1ndez A, Barrenechea E, Herrera F. EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition, 2013, 46(12): 3460\u20133471","journal-title":"Pattern Recognition"},{"issue":"2","key":"7182_CR26","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-011-0465-6","volume":"33","author":"E Ramentol","year":"2012","unstructured":"Ramentol E, Caballero Y, Bello R, Herrera F. SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. Knowledge and Information Systems, 2012, 33(2): 245\u2013265","journal-title":"Knowledge and Information Systems"},{"issue":"4","key":"7182_CR27","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/TSMCB.2012.2187280","volume":"42","author":"S Wang","year":"2012","unstructured":"Wang S, Yao X. Multiclass imbalance problems: analysis and potential solutions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(4): 1119\u20131130","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part^B (Cybernetics)"},{"key":"7182_CR28","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1109\/IJCNN.2014.6889667","volume-title":"Proceedings of the 2014 International Joint Conference on Neural Networks","author":"X Y Liu","year":"2014","unstructured":"Liu X Y, Li Q Q. Learning from combination of data chunks for multiclass imbalanced data. In: Proceedings of the 2014 International Joint Conference on Neural Networks. 2014, 1680\u20131687"},{"key":"7182_CR29","first-page":"1826","volume-title":"Proceedings of the 20th International Joint Conference on Artificial Intelligence","author":"S Li","year":"2011","unstructured":"Li S, Wang Z, Zhou G, Lee S Y M. Semi-supervised learning for imbalanced sentiment classification. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence. 2011, 1826\u20131832"},{"key":"7182_CR30","first-page":"4041","volume-title":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","author":"M L Zhang","year":"2015","unstructured":"Zhang M L, Li Y K, Liu X Y. Towards class-imbalance aware multilabel learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. 2015, 4041\u20134047"},{"key":"7182_CR31","first-page":"168","volume-title":"Proceedings of the 18th^ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"T R Hoens","year":"2012","unstructured":"Hoens T R, Chawla N V. Learning in non-stationary environments with class imbalance. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 168\u2013176"},{"issue":"5","key":"7182_CR32","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2015","unstructured":"Wang S, Minku L L, Yao X. Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1356\u20131368","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"7182_CR33","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1109\/ICDM.2011.137","volume-title":"Proceeding of the 11st^IEEE International Conference on Data Mining","author":"H Cao","year":"2011","unstructured":"Cao H, Li X L, Woon Y K, Ng S K. SPO: structure preserving oversampling for imbalanced time series classification. In: Proceeding of the 11st IEEE International Conference on Data Mining. 2011, 1008\u20131013"},{"issue":"12","key":"7182_CR34","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.1109\/TKDE.2013.37","volume":"25","author":"H Cao","year":"2013","unstructured":"Cao H, Li X L, Woon D Y K, Ng S K. Integrated oversampling for imbalanced time series classification. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2809\u20132822","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"7182_CR35","first-page":"107","volume-title":"Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases","author":"N V Chawla","year":"2003","unstructured":"Chawla N V, Lazarevic A, Hall L O, Bowyer K W. SMOTEBoost: improving prediction of the minority class in boosting. In: Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases. 2003, 107\u2013119"},{"key":"7182_CR36","first-page":"324","volume-title":"Proceedings of^IEEE Symposium on Computational Intelligence and Data Mining","author":"S Wang","year":"2009","unstructured":"Wang S, Yao X. Diversity analysis on imbalanced data sets by using ensemble models. In: Proceedings of IEEE Symposium on Computational Intelligence and Data Mining. 2009, 324\u2013331"},{"issue":"12","key":"7182_CR37","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun Y, Kamel M S, Wong A K, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition, 2007, 40(12): 3358\u20133378","journal-title":"Pattern Recognition"},{"issue":"1","key":"7182_CR38","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2010","unstructured":"Seiffert C, Khoshgoftaar T M, Van Hulse J, Napolitano A. RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A (Systems and Humans), 2010, 40(1): 185\u2013197","journal-title":"IEEE Transactions on Systems, Man and Cybernetics, Part^A (Systems and Humans)"},{"key":"7182_CR39","first-page":"769","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek I. Two modifications of CNN. IEEE Transactions of System Man Cybernetics, 1976, 6: 769\u2013772","journal-title":"IEEE Transactions of System Man Cybernetics"},{"key":"7182_CR40","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1145\/1273496.1273592","volume-title":"Proceedings of the 24th International Conference on Machine Learning","author":"R Raina","year":"2007","unstructured":"Raina R, Battle A, Lee H, Packer B, Ng A Y. Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 759\u2013766"},{"key":"7182_CR41","first-page":"1338","volume-title":"Proceedings of the 13rd^AAAI Conference on Artificial Intelligence","author":"Y Wei","year":"2016","unstructured":"Wei Y, Zhu Y, Leung C W, Song Y, Yang Q. Instilling social to physical: co-regularized heterogeneous transfer learning. In: Proceedings of the 13rd AAAI Conference on Artificial Intelligence. 2016, 1338\u20131344"},{"issue":"1","key":"7182_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar T M, Wang D. A survey of transfer learning. Journal of Big Data, 2016, 3(1): 1\u201340","journal-title":"Journal of Big Data"},{"issue":"1","key":"7182_CR43","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s10115-015-0870-3","volume":"48","author":"S Al-Stouhi","year":"2016","unstructured":"Al-Stouhi S, Reddy C K. Transfer learning for class imbalance problems with inadequate data. Knowledge and Information Systems, 2016, 48(1): 201\u2013208","journal-title":"Knowledge and Information Systems"},{"issue":"4","key":"7182_CR44","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1002\/sam.11217","volume":"7","author":"L Ge","year":"2014","unstructured":"Ge L, Gao J, Ngo H, Li K, Zhang A. On handling negative transfer and imbalanced distributions in multiple source transfer learning. Statistical Analysis and Data Mining, 2014, 7(4): 254\u2013271","journal-title":"Statistical Analysis and Data Mining"},{"key":"7182_CR45","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1145\/1273496.1273521","volume-title":"Proceedings of the 24th International Conference on Machine Learning","author":"W Dai","year":"2007","unstructured":"Dai W, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 193\u2013200"},{"key":"7182_CR46","volume-title":"UCI repository of machine learning databases. University of California","author":"C Blake","year":"1996","unstructured":"Blake C, Keogh E, Merz C J. UCI repository of machine learning databases. University of California, Irvine, CA, 1996"},{"key":"7182_CR47","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"2017","unstructured":"Breiman L, Friedman J, Olshen R A, Stone C J. Classification and Regression Trees. London: Routledge Press, 2017"},{"key":"7182_CR48","first-page":"1401","volume-title":"Proceedings of the 16th International Joint Conference on Artificial Intelligence","author":"R E Schapire","year":"1999","unstructured":"Schapire R E. A brief introduction to Boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence. 1999, 1401\u20131406"},{"issue":"3","key":"7182_CR49","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10044-003-0192-z","volume":"6","author":"R Barandela","year":"2003","unstructured":"Barandela R, Valdovinos R M, Snchez J S. New applications of ensembles of classifiers. Pattern Analysis and Applications, 2003, 6(3): 245\u2013256","journal-title":"Pattern Analysis and Applications"},{"issue":"2","key":"7182_CR50","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123\u2013140","journal-title":"Machine Learning"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-018-7182-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11704-018-7182-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-018-7182-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T07:02:01Z","timestamp":1662706921000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11704-018-7182-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,17]]},"references-count":50,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["7182"],"URL":"https:\/\/doi.org\/10.1007\/s11704-018-7182-1","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,17]]},"assertion":[{"value":"28 May 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}