{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T19:14:47Z","timestamp":1775675687495,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T00:00:00Z","timestamp":1588809600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T00:00:00Z","timestamp":1588809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976089"],"award-info":[{"award-number":["61976089"]}],"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":["61473259"],"award-info":[{"award-number":["61473259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s13042-020-01131-5","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T11:03:08Z","timestamp":1588849388000},"page":"2607-2624","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Fast feature selection for interval-valued data through kernel density estimation entropy"],"prefix":"10.1007","volume":"11","author":[{"given":"Jianhua","family":"Dai","sequence":"first","affiliation":[]},{"given":"Ye","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jiaolong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,7]]},"reference":[{"issue":"4","key":"1131_CR1","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1007\/s13042-016-0595-y","volume":"9","author":"MM Javidi","year":"2018","unstructured":"Javidi MM, Eskandari S (2018) Streamwise feature selection: a rough set method. Int J Mach Learn Cybernet 9(4):667\u2013676","journal-title":"Int J Mach Learn Cybernet"},{"issue":"4","key":"1131_CR2","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s13042-017-0758-5","volume":"10","author":"JZ Li","year":"2019","unstructured":"Li JZ, Yang XB, Song XN, Wang PX, Yu DJ (2019) Neighborhood attribute reduction: a multi-criterion approach. Int J Mach Learn Cybernet 10(4):731\u2013742","journal-title":"Int J Mach Learn Cybernet"},{"issue":"2","key":"1131_CR3","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TFUZZ.2017.2698420","volume":"26","author":"JH Dai","year":"2018","unstructured":"Dai JH, Hu QH, Hu H, Huang DB (2018) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26(2):937\u2013950","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"1131_CR4","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s13042-017-0760-y","volume":"10","author":"RH Shang","year":"2019","unstructured":"Shang RH, Chang JW, Jiao LC, Xue Y (2019) Unsupervised feature selection based on self-representation sparse regression and local similarity preserving. Int J Mach Learn Cybernet 10(4):757\u2013770","journal-title":"Int J Mach Learn Cybernet"},{"issue":"9","key":"1131_CR5","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1109\/TCYB.2016.2636339","volume":"47","author":"JH Dai","year":"2017","unstructured":"Dai JH, Hu QH, Zhang JH, Hu H, Zheng NG (2017) Attribute selection for partially labeled categorical data by rough set approach. IEEE Trans Cybernet 47(9):2460\u20132471","journal-title":"IEEE Trans Cybernet"},{"key":"1131_CR6","first-page":"43","volume":"240","author":"JH Dai","year":"2013","unstructured":"Dai JH (2013) Rough set approach to incomplete numerical data. Inf Sci 240:43\u201357","journal-title":"Inf Sci"},{"issue":"4","key":"1131_CR7","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TFUZZ.2016.2574918","volume":"25","author":"CZ Wang","year":"2017","unstructured":"Wang CZ, Qi YL, Shao MW, Hu QH, Chen DG, Qian YH, Lin YJ (2017) A fitting model for feature selection with fuzzy rough sets. IEEE Trans Fuzzy Syst 25(4):741\u2013753","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"1131_CR8","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1109\/TFUZZ.2017.2768044","volume":"26","author":"JH Dai","year":"2018","unstructured":"Dai JH, Hu H, Wu WZ, Qian YH, Huang DB (2018) Maximal-discernibility-pair-based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26(4):2174\u20132187","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1131_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2016.02.013","volume":"56","author":"X Zhang","year":"2016","unstructured":"Zhang X, Mei CL, Chen DG, Li JH (2016) Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy. Pattern Recogn 56:1\u201315","journal-title":"Pattern Recogn"},{"issue":"1","key":"1131_CR10","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.asoc.2012.07.029","volume":"13","author":"JH Dai","year":"2013","unstructured":"Dai JH, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211\u2013221","journal-title":"Appl Soft Comput"},{"key":"1131_CR11","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.knosys.2016.04.002","volume":"102","author":"JH Dai","year":"2016","unstructured":"Dai JH, Han HF, Hu QH, Liu MF (2016) Discrete particle swarm optimization approach for cost sensitive attribute reduction. Knowl-Based Syst 102:116\u2013126","journal-title":"Knowl-Based Syst"},{"key":"1131_CR12","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.asoc.2018.05.003","volume":"69","author":"AS Ashour","year":"2018","unstructured":"Ashour AS, Guo Y, Kucukkulahli E, Erdogmus P, Polat K (2018) A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation. Appl Soft Comput 69:426\u2013434","journal-title":"Appl Soft Comput"},{"issue":"33","key":"1131_CR13","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"3","author":"E Parzen","year":"1962","unstructured":"Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 3(33):1065\u20131076","journal-title":"Ann Math Stat"},{"key":"1131_CR14","doi-asserted-by":"crossref","unstructured":"Rosenblatt M (1956) Remarks on some nonparametric estimates of a density function. Ann Math Stat, pp 832\u2013837","DOI":"10.1214\/aoms\/1177728190"},{"issue":"9","key":"1131_CR15","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.1109\/TIP.2010.2047667","volume":"19","author":"A Banerjee","year":"2010","unstructured":"Banerjee A, Burlina P (2010) Efficient particle filtering via sparse kernel density estimation. IEEE Trans Image Process 19(9):2480\u20132490","journal-title":"IEEE Trans Image Process"},{"key":"1131_CR16","first-page":"1","volume":"27","author":"XJ Cai","year":"2012","unstructured":"Cai XJ, Wu ZF, Cheng J (2012) Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation. Int J Geogr Inf Sci 27:1\u20139","journal-title":"Int J Geogr Inf Sci"},{"issue":"2","key":"1131_CR17","doi-asserted-by":"crossref","first-page":"179","DOI":"10.3724\/SP.J.1004.2011.00179","volume":"37","author":"PJ Qian","year":"2011","unstructured":"Qian PJ, Wang ST, Deng ZH (2011) Fast adaptive similarity-based clustering using sparse parzen window density estimation. Acta Autom Sin 37(2):179\u2013187","journal-title":"Acta Autom Sin"},{"issue":"3","key":"1131_CR18","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TSTE.2016.2530049","volume":"7","author":"M Rouhani","year":"2016","unstructured":"Rouhani M, Mohammadi M, Kargarian A (2016) Parzen window density estimator-based probabilistic power flow with correlated uncertainties. IEEE Trans Sustain Energy 7(3):1170\u20131181","journal-title":"IEEE Trans Sustain Energy"},{"issue":"6","key":"1131_CR19","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/S0893-6080(05)80086-3","volume":"5","author":"H Schller","year":"1992","unstructured":"Schller H, Hartmann U (1992) Mapping neural network derived from the parzen window estimator. Neural Netw 5(6):903\u2013909","journal-title":"Neural Netw"},{"issue":"1","key":"1131_CR20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.patcog.2007.03.029","volume":"41","author":"S Wang","year":"2008","unstructured":"Wang S, Chung F, Xiong F (2008) A novel image thresholding method based on parzen window estimate. Pattern Recogn 41(1):117\u2013129","journal-title":"Pattern Recogn"},{"key":"1131_CR21","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.eswa.2015.12.031","volume":"51","author":"SC Wang","year":"2016","unstructured":"Wang SC, Gao R, Wang LM (2016) Bayesian network classifiers based on gaussian kernel density. Expert Syst Appl 51:207\u2013217","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1131_CR22","doi-asserted-by":"crossref","first-page":"e88825","DOI":"10.1371\/journal.pone.0088825","volume":"9","author":"SS Yang","year":"2014","unstructured":"Yang SS, Zheng F, Luo X, Cai SX, Wu YF, Liu KZ, Wu MH, Chen J, Krishnan S (2014) Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with parkinsons disease. PLoS ONE 9(2):e88825","journal-title":"PLoS ONE"},{"key":"1131_CR23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jtrangeo.2015.04.008","volume":"45","author":"WH Yu","year":"2015","unstructured":"Yu WH, Ai TH, Shao SW (2015) The analysis and delimitation of central business district using network kernel density estimation. J Transp Geogr 45:32\u201347","journal-title":"J Transp Geogr"},{"issue":"12","key":"1131_CR24","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TPAMI.2002.1114861","volume":"24","author":"N Kwak","year":"2002","unstructured":"Kwak N, Choi CH (2002) Input feature selection by mutual information based on parzen window. IEEE Trans Pattern Anal Mach Intell 24(12):1667\u20131671","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1131_CR25","doi-asserted-by":"crossref","unstructured":"Xu SQ, Dai JH, Shi H (2018) Semi-supervised feature selection by mutual information based on kernel density estimation. In: 24th international conference on pattern recognition (ICPR), pp 818\u2013823","DOI":"10.1109\/ICPR.2018.8546181"},{"key":"1131_CR26","unstructured":"Zhang JH (2017) Kernel density estimation entropy for mixed data and fast greedy feature selection algorithms. Master\u2019s thesis, Zhejiang university"},{"key":"1131_CR27","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.knosys.2011.10.013","volume":"27","author":"JH Dai","year":"2012","unstructured":"Dai JH, Wang WT, Xu Q, Tian HW (2012) Uncertainty measurement for interval-valued decision systems based on extended conditional entropy. Knowl-Based Syst 27:443\u2013450","journal-title":"Knowl-Based Syst"},{"key":"1131_CR28","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.ins.2013.06.047","volume":"251","author":"JH Dai","year":"2013","unstructured":"Dai JH, Wang WT, Mi JS (2013) Uncertainty measurement for interval-valued information systems. Inf Sci 251:63\u201378","journal-title":"Inf Sci"},{"key":"1131_CR29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.ins.2014.02.070","volume":"271","author":"WS Du","year":"2014","unstructured":"Du WS, Hu BQ (2014) Approximate distribution reducts in inconsistent interval-valued ordered decision tables. Inf Sci 271:93\u2013114","journal-title":"Inf Sci"},{"key":"1131_CR30","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.ins.2014.10.003","volume":"294","author":"XB Yang","year":"2015","unstructured":"Yang XB, Qi Yong YDJ, Yu HL, Yang JY (2015) $$\\alpha$$-Dominance relation and rough sets in interval-valued information systems. Inf Sci 294:334\u2013347","journal-title":"Inf Sci"},{"issue":"1","key":"1131_CR31","first-page":"701","volume":"32","author":"JH Dai","year":"2017","unstructured":"Dai JH, Zheng GJ, Han HF, Hu QH, Zheng NG, Liu J, Zhang QL (2017) Probability approach for interval-valued ordered decision systems in dominance-based fuzzy rough set theory. J Intell Fuzzy Syst 32(1):701\u2013703","journal-title":"J Intell Fuzzy Syst"},{"key":"1131_CR32","doi-asserted-by":"crossref","first-page":"64","DOI":"10.4018\/IJCVIP.2017040105","volume":"7","author":"DS Guru","year":"2017","unstructured":"Guru DS, Kumar NV, Suhil M (2017) Feature selection of interval valued data through interval K-means clustering. Int J Comput Vis Image Process 7:64\u201380","journal-title":"Int J Comput Vis Image Process"},{"issue":"1","key":"1131_CR33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s13042-016-0577-0","volume":"8","author":"LF Li","year":"2017","unstructured":"Li LF (2017) Multi-level interval-valued fuzzy concept lattices and their attribute reduction. Int J Mach Learn Cybernet 8(1):45\u201356","journal-title":"Int J Mach Learn Cybernet"},{"issue":"9","key":"1131_CR34","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1631\/FITEE.1500447","volume":"17","author":"JH Dai","year":"2016","unstructured":"Dai JH, Hu H, Zheng GJ, Hu QH, Han HF, Shi H (2016) Attribute reduction in interval-valued information systems based on information entropies. Front Inf Technol Electron Eng 17(9):919\u2013928","journal-title":"Front Inf Technol Electron Eng"},{"key":"1131_CR35","doi-asserted-by":"crossref","first-page":"423","DOI":"10.3233\/JIFS-17178","volume":"34","author":"JH Dai","year":"2018","unstructured":"Dai JH, Yan YJ, Li ZW, Liao BS (2018) Dominance-based fuzzy rough set approach for incomplete interval-valued data. J Intell Fuzzy Syst 34:423\u2013436","journal-title":"J Intell Fuzzy Syst"},{"key":"1131_CR36","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1007\/978-3-030-16660-1_67","volume":"941","author":"DS Guru","year":"2020","unstructured":"Guru DS, Kumar NV (2020) Interval chi-square score (ICSS): feature selection of interval valued data. Adv Intell Syst Comput 941:686\u2013698","journal-title":"Adv Intell Syst Comput"},{"key":"1131_CR37","volume-title":"Inf Theory and Entropy","author":"RA Gatenby","year":"2008","unstructured":"Gatenby RA, Frieden BR (2008) Inf Theory and Entropy. Springer, New York"},{"issue":"5","key":"1131_CR38","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1109\/TFUZZ.2014.2371479","volume":"23","author":"XZ Wang","year":"2015","unstructured":"Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638\u20131654","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"6","key":"1131_CR39","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TFUZZ.2017.2717803","volume":"25","author":"R Wang","year":"2017","unstructured":"Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460\u20131475","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"1131_CR40","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TCYB.2017.2653223","volume":"48","author":"XZ Wang","year":"2018","unstructured":"Wang XZ, Wang R, Xu C (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybernet 48(2):703\u2013715","journal-title":"IEEE Trans Cybernet"},{"key":"1131_CR41","first-page":"127","volume":"6","author":"GL Zhang","year":"2015","unstructured":"Zhang GL, Shen H, Shi F, Huo YQ (2015) Block iterative inversion algorithms for large real symmetric matrix. Wirel Interconnect Technol 6:127\u2013129","journal-title":"Wirel Interconnect Technol"},{"issue":"6","key":"1131_CR42","first-page":"782","volume":"58","author":"J Grcar","year":"2011","unstructured":"Grcar J (2011) Mathematicians of Gaussian elimination. Not Am Math Soc 58(6):782\u2013792","journal-title":"Not Am Math Soc"},{"issue":"9","key":"1131_CR43","first-page":"4667","volume":"219","author":"PS Stanimirovi\u0107","year":"2013","unstructured":"Stanimirovi\u0107 PS, Petkovi\u0107 MD (2013) Gauss-Jordan elimination method for computing outer inverses. Appl Math Comput 219(9):4667\u20134679","journal-title":"Appl Math Comput"},{"issue":"4","key":"1131_CR44","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.patrec.2010.11.018","volume":"32","author":"L Hedjazi","year":"2011","unstructured":"Hedjazi L, Aguilar MJ, Lann MVL (2011) Similarity-margin based feature selection for symbolic interval data. Pattern Recogn Lett 32(4):578\u2013585","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"1131_CR45","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.conengprac.2010.03.003","volume":"18","author":"J Quevedo","year":"2010","unstructured":"Quevedo J, Puig V, Cembrano G, Blanch J, Aguilar J, Saporta D, Benito G, Hedo M, Molina A (2010) Validation and reconstruction of flow meter data in the barcelona water distribution network. Control Eng Pract 18(6):640\u2013651","journal-title":"Control Eng Pract"},{"issue":"6","key":"1131_CR46","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","volume":"7","author":"J Khan","year":"2001","unstructured":"Khan J, Wei JS, Ringn\u00e9r M, Lao HS, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673\u2013679","journal-title":"Nat Med"},{"issue":"4","key":"1131_CR47","first-page":"1","volume":"9","author":"JD Li","year":"2018","unstructured":"Li JD, Cheng KW, Wang SH, Morstatter F, Trevino RP, Tang JL, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv 9(4):1\u201345","journal-title":"ACM Comput Surv"},{"key":"1131_CR48","unstructured":"Dua D, Graff C (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"1131_CR49","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ins.2016.09.018","volume":"373","author":"YY Zhang","year":"2016","unstructured":"Zhang YY, Li TR, Luo C, Zhang JB, Chen HM (2016) Incremental updating of rough approximations in interval-valued information systems under attribute generalization. Inf Sci 373:461\u2013475","journal-title":"Inf Sci"},{"key":"1131_CR50","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.knosys.2017.09.009","volume":"136","author":"JH Dai","year":"2017","unstructured":"Dai JH, Wei BJ, Zhang XH, Zhang QL (2017) Uncertainty measurement for incomplete interval-valued information systems based on $$\\alpha$$-weak similarity. Knowl-Based Syst 136:159\u2013171","journal-title":"Knowl-Based Syst"},{"key":"1131_CR51","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.3233\/IDA-150778","volume":"19","author":"DC He","year":"2015","unstructured":"He DC, Zhang HJ, Hao WN, Zhang R (2015) A robust parzen window mutual information estimator for feature selection with label noise. Intell Data Anal 19:1199\u20131212","journal-title":"Intell Data Anal"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01131-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01131-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01131-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T20:12:47Z","timestamp":1696104767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01131-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,7]]},"references-count":51,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1131"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01131-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,7]]},"assertion":[{"value":"29 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}