{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T03:42:15Z","timestamp":1768707735080,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100011447","name":"Science and Technology Department of Henan Province","doi-asserted-by":"publisher","award":["182102210132"],"award-info":[{"award-number":["182102210132"]}],"id":[{"id":"10.13039\/501100011447","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802329"],"award-info":[{"award-number":["61802329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Innovation Team Support Plan of the University of Science and Technology of Henan Province","award":["No. 19IRTSTHN014"],"award-info":[{"award-number":["No. 19IRTSTHN014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00500-019-04501-6","type":"journal-article","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T12:03:21Z","timestamp":1573473801000},"page":"4711-4727","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Ensemble learning via constraint projection and undersampling technique for class-imbalance problem"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6585-1805","authenticated-orcid":false,"given":"Huaping","family":"Guo","sequence":"first","affiliation":[]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Chang-an","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"issue":"2\u20133","key":"4501_CR1","first-page":"255","volume":"17","author":"J Alcal\u00e1-Fdez","year":"2011","unstructured":"Alcal\u00e1-Fdez J, Fernandez A, Luengo J, Derrac J, Garc\u00eda S, S\u00e1nchez L, Herrera F (2011) KEEL data-mining software tool: data set repository integration of algorithms and experimental analysis framework. J Multiple Valued Logic Soft Comput 17(2\u20133):255\u2013287","journal-title":"J Multiple Valued Logic Soft Comput"},{"key":"4501_CR2","unstructured":"Ameta D (2017) Ensemble classifier approach in breast cancer detection and malignancy grading-a review, CoRR abs\/1704.03801"},{"key":"4501_CR3","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.neucom.2014.05.096","volume":"172","author":"L Bao","year":"2016","unstructured":"Bao L, Juan C, Li J, Zhang Y (2016b) Boosted near-miss under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets. Neurocomputing 172:198\u2013206","journal-title":"Neurocomputing"},{"issue":"3","key":"4501_CR4","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 RM, S\u00e1nchez JS (2003) New applications of ensembles of classifiers. Pattern Anal Appl 6(3):245\u2013256","journal-title":"Pattern Anal Appl"},{"issue":"2","key":"4501_CR5","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2014","unstructured":"Barua S, Islam MM, Yao X, Murase K (2014) MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405\u2013425","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4501_CR6","unstructured":"Branco P, Torgo L, Ribeiro RP (2015) A survey of predictive modelling under imbalanced distributions, CoRR abs\/1505.01658 D"},{"issue":"2","key":"4501_CR7","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"4501_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"4501_CR9","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-smote: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Proceedings of the 13th Pacific-Asia conference on knowledge discovery and data mining(PAKDD), lecture notes in computer science, vol 5476. Springer, Bangkok, Thailand, pp 475\u2013482","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"4501_CR10","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.patrec.2018.01.003","volume":"103","author":"FJ Castellanos","year":"2018","unstructured":"Castellanos FJ, Valero-Mas JJ, Calvo-Zaragoza J, Rico-Juan JR (2018) Oversampling imbalanced data in the string space. Pattern Recognit Lett 103:32\u201338","journal-title":"Pattern Recognit Lett"},{"key":"4501_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"4501_CR12","doi-asserted-by":"crossref","unstructured":"Chawla N V, Lazarevic A, Hall LO, Bowyer K W (2003) 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, lecture notes in computer science, vol 2838. Springer, Cavtat-Dubrovnik, Croatia, pp 107\u2013119","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"4501_CR13","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"4501_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2016.10.006","volume":"93","author":"D Devi","year":"2017","unstructured":"Devi D, Biswas SK, Purkayastha B (2017) Redundancy-driven modified tomek-link based undersampling: a solution to class imbalance. Pattern Recognit Lett 93:3\u201312","journal-title":"Pattern Recognit Lett"},{"key":"4501_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","volume":"465","author":"G Douzas","year":"2018","unstructured":"Douzas G, Ba\u00e7\u00e3o F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci 465:1\u201320","journal-title":"Inf Sci"},{"key":"4501_CR16","doi-asserted-by":"crossref","unstructured":"Estabrooks A, Japkowicz N (2001) A mixture-of-experts framework for learning from imbalanced data sets. In: Proceeding of the 4th international conference on advances in intelligent data analysis, lecture notes in computer science, vol 2189. Springer, Cascais, Portugal, pp 34\u201343","DOI":"10.1007\/3-540-44816-0_4"},{"key":"4501_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-98074-4","volume-title":"Learning from imbalanced data sets","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Galar M, Prati RC, Krawczyk B, Herrera F (2018) Learning from imbalanced data sets. Springer, Berlin, pp 1\u2013377"},{"key":"4501_CR18","unstructured":"Frayman Y, Ming Ting K, Wang L (1999) A fuzzy neural network for data mining: dealing with the problem of small disjuncts. In: Proceeding of international joint conference neural networks, pp 2490\u20132493. Washington, DC, USA"},{"key":"4501_CR19","unstructured":"Freund Y, Schapire R E (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning, pp 148\u201315. Morgan Kaufmann, Bari, Italy"},{"key":"4501_CR20","unstructured":"Fu X, Wang L, Chua K S (2002) Training RBF neural networks on unbalanced data. In: Proceedings of the 9th international conference on neural information processing (ICONIP 2002), Singapore, pp 1016\u20131020"},{"issue":"4","key":"4501_CR21","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fern\u00e1ndez A, Tartas EB, Sola HB, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C 42(4):463\u2013484","journal-title":"IEEE Trans Syst Man Cybern Part C"},{"key":"4501_CR22","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044\u20132064","journal-title":"Inf Sci"},{"key":"4501_CR23","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ins.2018.03.002","volume":"445\u2013446","author":"S Garc\u00eda","year":"2018","unstructured":"Garc\u00eda S, Zhang Z, Altalhi AH, Alshomrani S, Herrera F (2018) Dynamic ensemble selection for multi-class imbalanced datasets. Inf Sci 445\u2013446:22\u201337","journal-title":"Inf Sci"},{"key":"4501_CR24","first-page":"1","volume":"8","author":"N Garc\u00eda-Pddrajas","year":"2007","unstructured":"Garc\u00eda-Pddrajas N, Garc\u00eda-Osorio C, Fyfe C (2007) Nonlinear boosting projections for ensemble construction. J Mach Learn Res 8:1\u201333","journal-title":"J Mach Learn Res"},{"key":"4501_CR25","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"H Guo","year":"2017","unstructured":"Guo H, Li Y, Shang J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239","journal-title":"Expert Syst Appl"},{"key":"4501_CR26","doi-asserted-by":"crossref","unstructured":"Han H, Wang W, Mao B (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Proceedings of the 1st international conference on intelligent computing (part I), lecture notes in computer science, vol 3644. Springer, Hefei, China, pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"key":"4501_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/9781118646106","volume-title":"Editors, imbalanced learning: foundations, algorithms, and applications","author":"H He","year":"2013","unstructured":"He H, Ma Y (2013) Editors, imbalanced learning: foundations, algorithms, and applications. Wiley, New York"},{"issue":"5\u20136","key":"4501_CR28","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1002\/sam.10061","volume":"2","author":"S Hido","year":"2009","unstructured":"Hido S, Kashima H, Takahashi Y (2009) Roughly balanced bagging for imbalanced data. Stat Anal Data Min 2(5\u20136):412\u2013426","journal-title":"Stat Anal Data Min"},{"issue":"12","key":"4501_CR29","doi-asserted-by":"publisher","first-page":"3047","DOI":"10.1109\/TCYB.2015.2496174","volume":"46","author":"S Huang","year":"2016","unstructured":"Huang S, Wang H, Li T, Yang Y, Li T (2016) Constraint co-projections for semi-supervised co-clustering. IEEE Trans Cybern 46(12):3047\u20133058","journal-title":"IEEE Trans Cybern"},{"key":"4501_CR30","doi-asserted-by":"crossref","unstructured":"Kang P, Cho S (2006) EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems. In: Proceedings of the 13th international conference on neural information processing, part I, lecture notes in computer science, vol 4232. Springer, Hong Kong, China, pp 837\u2013846","DOI":"10.1007\/11893028_93"},{"issue":"2\u20133","key":"4501_CR31","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1023\/A:1007452223027","volume":"30","author":"M Kubat","year":"1998","unstructured":"Kubat M, Holte RC, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30(2\u20133):195\u2013215","journal-title":"Mach Learn"},{"key":"4501_CR32","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1016\/j.jspi.2007.04.032","volume":"138","author":"J Li","year":"2008","unstructured":"Li J (2008) A two-step rejection procedure for testing multiple hypotheses. J Stat Plan Inference 138:1521\u20131527","journal-title":"J Stat Plan Inference"},{"key":"4501_CR33","doi-asserted-by":"crossref","unstructured":"Liu X Y, Wu J, Zhou Z H (2006) Exploratory under-sampling for class-imbalance learning. In: Proceedings of the 6th IEEE international conference on data mining (ICDM), Hong Kong, China, pp 965\u2013969","DOI":"10.1109\/ICDM.2006.68"},{"issue":"2","key":"4501_CR34","first-page":"965","volume":"39","author":"XY Liu","year":"2009","unstructured":"Liu XY, Wu J, Zhou ZH (2009) Exploratory under-sampling for class-imbalance learning. IEEE Trans Syst Man Cybern Part B 39(2):965\u2013969","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"key":"4501_CR35","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.jss.2017.07.006","volume":"132","author":"W Lu","year":"2017","unstructured":"Lu W, Li Z, Chu J (2017) Adaptive ensemble undersampling-boost: a novel learning framework for imbalanced data. J Syst Softw 132:272\u2013282","journal-title":"J Syst Softw"},{"issue":"1","key":"4501_CR36","first-page":"37","volume":"2","author":"PD Martin","year":"2011","unstructured":"Martin PD (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2(1):37\u201363","journal-title":"J Mach Learn Technol"},{"issue":"1","key":"4501_CR37","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJKESDP.2011.039875","volume":"3","author":"HM Nguyen","year":"2011","unstructured":"Nguyen HM, Cooper EW, Kamei K (2011) Borderline over-sampling for imbalanced data classification. Int J Knowl Eng Soft Data Paradig 3(1):4\u201321","journal-title":"Int J Knowl Eng Soft Data Paradig"},{"key":"4501_CR38","unstructured":"Provost F J, Weiss G M (2011) Learning when training data are costly: the effect of class distribution on tree induction, CoRR abs\/1106.4557"},{"key":"4501_CR39","volume-title":"C4.5: programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo"},{"key":"4501_CR40","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.compmedimag.2016.07.011","volume":"55","author":"F Ren","year":"2017","unstructured":"Ren F, Cao P, Li W, Zhao D, Za\u00efane OR (2017) Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm. Comput Med Imaging Graph 55:54\u201367","journal-title":"Comput Med Imaging Graph"},{"issue":"10","key":"4501_CR41","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","volume":"28","author":"JJ Rodr\u00edguez","year":"2006","unstructured":"Rodr\u00edguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619\u20131630","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4501_CR42","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fdez I, Canosa A, Mucientes M, Bugar\u00edn A (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: Proceeding of the IEEE international conference on fuzzy systems, pp 1\u20138. FUZZ-IEEE, Istanbul, Turkey","DOI":"10.1109\/FUZZ-IEEE.2015.7337889"},{"issue":"1\u20132","key":"4501_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1\u20132):1\u201339","journal-title":"Artif Intell Rev"},{"key":"4501_CR44","unstructured":"Schapire RE (1999) A brief introduction to boosting. In: Proceedings of the 16th international joint conference on artificial intelligence, pp 1401\u20131406. Morgan Kaufmann, Stockholm, Sweden"},{"issue":"1","key":"4501_CR45","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, Hulse JV, Napolitano A (2010) Rusboost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern Part A 40(1):185\u2013197","journal-title":"IEEE Trans Syst Man Cybern Part A"},{"issue":"2","key":"4501_CR46","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s11704-016-5306-z","volume":"12","author":"B Sun","year":"2018","unstructured":"Sun B, Chen H, Wang J, Xie H (2018a) Evolutionary under-sampling based bagging ensemble method for imbalanced data classification. Front Comput Sci 12(2):331\u2013350","journal-title":"Front Comput Sci"},{"key":"4501_CR47","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ins.2017.10.017","volume":"425","author":"J Sun","year":"2018","unstructured":"Sun J, Lang J, Fujita H, Li H (2018b) Imbalanced enterprise credit evaluation with DTE-SBD: decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Inf Sci 425:76\u201391","journal-title":"Inf Sci"},{"issue":"2","key":"4501_CR48","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.jbi.2008.09.001","volume":"42","author":"LM Taft","year":"2009","unstructured":"Taft LM, Evans RS, Shyu CR, Egger MJ, Chawla N, Mitchell JA, Thornton SN, Bray B, Varner Michael W (2009) Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. J Biomed Inform 42(2):356\u2013364","journal-title":"J Biomed Inform"},{"issue":"7","key":"4501_CR49","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TPAMI.2006.134","volume":"28","author":"D Tao","year":"2006","unstructured":"Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088\u20131099","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4501_CR50","series-title":"Advanced information and knowledge processing","volume-title":"Data mining with computational intelligence","author":"L Wang","year":"2005","unstructured":"Wang L, Fu X (2005) Data mining with computational intelligence. Advanced information and knowledge processing. Springer, Berlin"},{"key":"4501_CR51","doi-asserted-by":"crossref","unstructured":"Wang S, Yao X (2009) Diversity analysis on imbalanced data sets by using ensemble models. In: Proceedings of the IEEE symposium on computational intelligence and data mining, part of the IEEE symposium series on computational intelligence, pp 324\u2013331. IEEE, Nashville, TN, USA","DOI":"10.1109\/CIDM.2009.4938667"},{"issue":"1","key":"4501_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2008","unstructured":"Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng AFM, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1\u201337","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"4501_CR53","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-140649","volume":"18","author":"J Zhai","year":"2014","unstructured":"Zhai J, Zhai M, Kang X (2014) Condensed fuzzy nearest neighbor methods based on fuzzy rough set technique. Intell Data Anal 18(3):429\u2013447","journal-title":"Intell Data Anal"},{"key":"4501_CR54","unstructured":"Zhang D, Chen S, Zhou Z, Yang Q (2008) Constraint projections for ensemble learning. In: Proceedings of the 23rd AAAI conference on artificial intelligence, pp 758\u2013763. AAAI Press, Chicago, Illinois, USA"},{"key":"4501_CR55","doi-asserted-by":"crossref","unstructured":"Zhou J, Guo H, Wu C-A (2018) Ensemble based on constraint projection and under-sampling for imbalanced learning. In: Proceeding of the 14th international conference on natural computation, fuzzy systems and knowledge discovery, (ICNC-FSKD 2018), pp 724\u2013731, IEEE, Huangshan, China","DOI":"10.1109\/FSKD.2018.8687174"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-04501-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-019-04501-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-04501-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:30:48Z","timestamp":1604968248000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-019-04501-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,11]]},"references-count":55,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4501"],"URL":"https:\/\/doi.org\/10.1007\/s00500-019-04501-6","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,11]]},"assertion":[{"value":"11 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Huaping Guo declares that he has no conflict of interest. Jun Zhou declares that he has no conflict of interest. Chang-an Wu declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}