{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:59:17Z","timestamp":1780765157477,"version":"3.54.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Funded by Science and Technology Project of Hebei Education Department","award":["BJ2025217"],"award-info":[{"award-number":["BJ2025217"]}]},{"name":"the Natural Science Key Research Project of the Education Department of Anhui Province","award":["2025AHGXZK30352"],"award-info":[{"award-number":["2025AHGXZK30352"]}]},{"name":"the Tangshan Kexue JishuJu, Applied Basic Research Project","award":["24130202C"],"award-info":[{"award-number":["24130202C"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02811-4","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:16:30Z","timestamp":1780758990000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Subspace ensemble learning by heterogeneous correlation collaborative ensemble for class imbalance problem"],"prefix":"10.1007","volume":"68","author":[{"given":"Qi","family":"Dai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"issue":"9","key":"2811_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 EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"2811_CR2","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B (2016) Learning from imbalanced data: Open challenges and future directions. Prog Artif Intell 5(4):221\u2013232","journal-title":"Prog Artif Intell"},{"issue":"18","key":"2811_CR3","doi-asserted-by":"publisher","first-page":"7637","DOI":"10.1039\/D5SC00270B","volume":"16","author":"J Jiang","year":"2025","unstructured":"Jiang J, Zhang C, Ke L, Hayes N, Zhu Y, Qiu H, Wei GW (2025) A review of machine learning methods for imbalanced data challenges in chemistry. Chem Sci 16(18):7637\u20137658","journal-title":"Chem Sci"},{"key":"2811_CR4","doi-asserted-by":"publisher","first-page":"121193","DOI":"10.1016\/j.ins.2024.121193","volume":"686","author":"LCM Liaw","year":"2025","unstructured":"Liaw LCM, Tan SC, Goh PY, Lim CP (2025) A histogram SMOTE-based sampling algorithm with incremental learning for imbalanced data classification. Inf Sci 686:121193","journal-title":"Inf Sci"},{"key":"2811_CR5","doi-asserted-by":"publisher","first-page":"110278","DOI":"10.1016\/j.engappai.2025.110278","volume":"147","author":"M Xia","year":"2025","unstructured":"Xia M, Wang Z, Lan X, Liu W, Wu J (2025) Confidence-driven under-sampling decision forest for imbalanced credit scoring. Eng Appl Artif Intell 147:110278","journal-title":"Eng Appl Artif Intell"},{"key":"2811_CR6","doi-asserted-by":"publisher","first-page":"107750","DOI":"10.1016\/j.future.2025.107750","volume":"167","author":"M Zheng","year":"2025","unstructured":"Zheng M, Wang F, Hu X, Hu L, Yu Q, Zheng X (2025) UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification. Future Gener Comput Syst 167:107750","journal-title":"Future Gener Comput Syst"},{"key":"2811_CR7","doi-asserted-by":"publisher","first-page":"131845","DOI":"10.1016\/j.eswa.2026.131845","volume":"316","author":"Q Dai","year":"2026","unstructured":"Dai Q, Wang L, Cao K, Du T, Ding W, Chen L (2026) KCPIA: Kolmogorov complexity-based positive instance augmentation for class-imbalance problem. Expert Syst Appl 316:131845","journal-title":"Expert Syst Appl"},{"key":"2811_CR8","doi-asserted-by":"publisher","first-page":"128918","DOI":"10.1016\/j.eswa.2025.128918","volume":"296","author":"L Gao","year":"2026","unstructured":"Gao L, Zhang M, Zhao S (2026) SMOMCCS: Minimum compact coverage oversampling approach for imbalanced data classification. Expert Syst Appl 296:128918","journal-title":"Expert Syst Appl"},{"issue":"3","key":"2811_CR9","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s10115-024-02281-6","volume":"67","author":"X Li","year":"2025","unstructured":"Li X, Liu Q (2025) A hybrid sampling algorithm for imbalanced and class-overlap data based on natural neighbors and density estimation. Knowl Inf Syst 67(3):2259\u20132290","journal-title":"Knowl Inf Syst"},{"issue":"10","key":"2811_CR10","doi-asserted-by":"publisher","first-page":"5905","DOI":"10.1109\/TKDE.2025.3589885","volume":"37","author":"Y Xie","year":"2025","unstructured":"Xie Y, Shan J, Wei L, Yao J, Zhou M (2025) GAN-based hybrid sampling method for transaction fraud detection. IEEE Trans Knowl Data Eng 37(10):5905\u20135918","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"2811_CR11","doi-asserted-by":"publisher","first-page":"2456","DOI":"10.1109\/TKDE.2025.3528719","volume":"37","author":"T Zhu","year":"2025","unstructured":"Zhu T, Hu X, Liu X, Zhu E, Zhu X, Xu H (2025) Dynamic ensemble framework for imbalanced data classification. IEEE Trans Knowl Data Eng 37(5):2456\u20132471","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"2811_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-025-01119-4","volume":"12","author":"M Carvalho","year":"2025","unstructured":"Carvalho M, Pinho AJ, Br\u00e1s S (2025) Resampling approaches to handle class imbalance: A review from a data perspective. J Big Data 12(1):71","journal-title":"J Big Data"},{"issue":"4","key":"2811_CR13","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1007\/s11063-022-10756-2","volume":"54","author":"YS Aurelio","year":"2022","unstructured":"Aurelio YS, de Almeida GM, de Castro CL, Braga AP (2022) Cost-sensitive learning based on performance metric for imbalanced data. Neural Process Lett 54(4):3097\u20133114","journal-title":"Neural Process Lett"},{"issue":"2","key":"2811_CR14","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1111\/coin.12566","volume":"39","author":"Q Dai","year":"2023","unstructured":"Dai Q, Liu JW, Yang J (2023) Multi-armed bandit heterogeneous ensemble learning for imbalanced data. Comput Intell 39(2):344\u2013368","journal-title":"Comput Intell"},{"key":"2811_CR15","doi-asserted-by":"publisher","first-page":"120223","DOI":"10.1016\/j.eswa.2023.120223","volume":"227","author":"B Zhu","year":"2023","unstructured":"Zhu B, Qian C, vanden Broucke S, Xiao J, Li Y (2023) A bagging-based selective ensemble model for churn prediction on imbalanced data. Expert Syst Appl 227:120223","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2811_CR16","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2009","unstructured":"Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2009) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern A Syst Hum 40(1):185\u2013197","journal-title":"IEEE Trans Syst Man Cybern A Syst Hum"},{"issue":"1","key":"2811_CR17","first-page":"2019","volume":"22","author":"Y Tian","year":"2021","unstructured":"Tian Y, Feng Y (2021) RaSE: random subspace ensemble classification. J Mach Learn Res 22(1):2019\u20132111","journal-title":"J Mach Learn Res"},{"issue":"5","key":"2811_CR18","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1109\/TNNLS.2021.3106306","volume":"34","author":"Y Xu","year":"2021","unstructured":"Xu Y, Yu Z, Chen CP, Liu Z (2021) Adaptive subspace optimization ensemble method for high-dimensional imbalanced data classification. IEEE Trans Neural Netw Learn Syst 34(5):2284\u20132297","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2811_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02344-8","author":"Y Jia","year":"2024","unstructured":"Jia Y, Zhu P (2024) Grouping attributes: an accelerator for attribute reduction based on similarity. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-024-02344-8","journal-title":"Int J Mach Learn Cybern"},{"key":"2811_CR20","doi-asserted-by":"publisher","first-page":"109083","DOI":"10.1016\/j.asoc.2022.109083","volume":"124","author":"Q Dai","year":"2022","unstructured":"Dai Q, Liu JW, Liu Y (2022) Multi-granularity relabeled under-sampling algorithm for imbalanced data. Appl Soft Comput 124:109083","journal-title":"Appl Soft Comput"},{"key":"2811_CR21","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.inffus.2022.08.017","volume":"89","author":"MS Santos","year":"2023","unstructured":"Santos MS, Abreu PH, Japkowicz N, Fern\u00e1ndez A, Santos J (2023) A unifying view of class overlap and imbalance: key concepts, multi-view panorama, and open avenues for research. Inf Fusion 89:228\u2013253","journal-title":"Inf Fusion"},{"key":"2811_CR22","doi-asserted-by":"publisher","first-page":"119735","DOI":"10.1016\/j.eswa.2023.119735","volume":"221","author":"Q Dai","year":"2023","unstructured":"Dai Q, Liu JW, Shi YH (2023) Class-overlap undersampling based on Schur decomposition for class-imbalance problems. Expert Syst Appl 221:119735","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2811_CR23","doi-asserted-by":"publisher","first-page":"4541","DOI":"10.1007\/s10489-022-03585-2","volume":"53","author":"G Wang","year":"2023","unstructured":"Wang G, Wang J, He K (2023) Majority-to-minority resampling for boosting-based classification under imbalanced data. Appl Intell 53(4):4541\u20134562","journal-title":"Appl Intell"},{"key":"2811_CR24","doi-asserted-by":"publisher","first-page":"108295","DOI":"10.1016\/j.knosys.2022.108295","volume":"242","author":"J Ren","year":"2022","unstructured":"Ren J, Wang Y, Mao M, Cheung YM (2022) Equalization ensemble for large scale highly imbalanced data classification. Knowl Based Syst 242:108295","journal-title":"Knowl Based Syst"},{"issue":"2","key":"2811_CR25","doi-asserted-by":"publisher","first-page":"103235","DOI":"10.1016\/j.ipm.2022.103235","volume":"60","author":"H Ding","year":"2023","unstructured":"Ding H, Sun Y, Wang Z, Huang N, Shen Z, Cui X (2023) RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification. Inf Process Manag 60(2):103235","journal-title":"Inf Process Manag"},{"issue":"4","key":"2811_CR26","doi-asserted-by":"publisher","first-page":"3559","DOI":"10.1007\/s40747-021-00614-4","volume":"9","author":"MZ Abedin","year":"2023","unstructured":"Abedin MZ, Guotai C, Hajek P, Zhang T (2023) Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex Intell Syst 9(4):3559\u20133579","journal-title":"Complex Intell Syst"},{"issue":"1","key":"2811_CR27","first-page":"2019","volume":"22","author":"Y Tian","year":"2022","unstructured":"Tian Y, Feng Y (2022) RaSE: Random subspace ensemble classification. J Mach Learn Res 22(1):2019\u20132111","journal-title":"J Mach Learn Res"},{"key":"2811_CR28","doi-asserted-by":"publisher","first-page":"107884","DOI":"10.1016\/j.asoc.2021.107884","volume":"113","author":"Z Wang","year":"2021","unstructured":"Wang Z, Jia P, Xu X, Wang B, Zhu Y, Li D (2021) Sample and feature selecting based ensemble learning for imbalanced problems. Appl Soft Comput 113:107884","journal-title":"Appl Soft Comput"},{"issue":"1","key":"2811_CR29","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TKDE.2021.3087517","volume":"35","author":"Y Xu","year":"2023","unstructured":"Xu Y, Yu Z, Cao W, Chen CP (2023) A novel classifier ensemble method based on subspace enhancement for high-dimensional data classification. IEEE Trans Knowl Data Eng 35(1):16\u201330","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2811_CR30","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1007\/s00521-020-05130-z","volume":"33","author":"E Elyan","year":"2021","unstructured":"Elyan E, Moreno-Garcia CF, Jayne C (2021) CDSMOTE: Class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural Comput Appl 33:2839\u20132851","journal-title":"Neural Comput Appl"},{"key":"2811_CR31","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":"2811_CR32","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ins.2019.08.062","volume":"509","author":"P Vuttipittayamongkol","year":"2020","unstructured":"Vuttipittayamongkol P, Elyan E (2020) Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Inf Sci 509:47\u201370","journal-title":"Inf Sci"},{"issue":"203","key":"2811_CR33","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1080\/01621459.1938.10502329","volume":"33","author":"J Berkson","year":"1938","unstructured":"Berkson J (1938) Some difficulties of interpretation encountered in the application of the chi-square test. J Am Stat Assoc 33(203):526\u2013536","journal-title":"J Am Stat Assoc"},{"issue":"7","key":"2811_CR34","first-page":"557","volume":"20","author":"S Wright","year":"1921","unstructured":"Wright S (1921) Correlation and causation. J Agric Res 20(7):557\u2013585","journal-title":"J Agric Res"},{"key":"2811_CR35","first-page":"23","volume":"460","author":"A Kumar","year":"2018","unstructured":"Kumar A, Abirami S (2018) Aspect-based opinion ranking framework for product reviews using a Spearman\u2019s rank correlation coefficient method. Inf Sci 460:23\u201341","journal-title":"Inf Sci"},{"key":"2811_CR36","first-page":"508","volume":"2","author":"H Abdi","year":"2007","unstructured":"Abdi H (2007) The Kendall rank correlation coefficient. Encyclopedia of measurement and statistics 2:508\u2013510","journal-title":"Encyclopedia of measurement and statistics"},{"issue":"2","key":"2811_CR37","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s00145-010-9084-8","volume":"24","author":"L Batina","year":"2011","unstructured":"Batina L, Gierlichs B, Prouff E, Rivain M, Standaert FX, Veyrat-Charvillon N (2011) Mutual information analysis: A comprehensive study. J Cryptol 24(2):269\u2013291","journal-title":"J Cryptol"},{"key":"2811_CR38","doi-asserted-by":"publisher","first-page":"4457","DOI":"10.1007\/s00521-020-05256-0","volume":"33","author":"BW Yuan","year":"2021","unstructured":"Yuan BW, Luo XG, Zhang ZL, Yu Y, Huo HW, Johannes T, Zou XD (2021) A novel density-based adaptive k nearest neighbor method for dealing with overlapping problem in imbalanced datasets. Neural Comput Appl 33:4457\u20134481","journal-title":"Neural Comput Appl"},{"issue":"3","key":"2811_CR39","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/34.667881","volume":"20","author":"J Kittler","year":"1998","unstructured":"Kittler J, Hatef M, Duin RP, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226\u2013239","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2811_CR40","first-page":"255","volume":"17","author":"J Derrac","year":"2015","unstructured":"Derrac J, Garcia S, Sanchez L, Herrera F (2015) Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Log Soft Comput 17:255\u2013287","journal-title":"J Mult Valued Log Soft Comput"},{"key":"2811_CR41","unstructured":"The UCI Machine Learning Repository, https:\/\/archive.ics.uci.edu"},{"key":"2811_CR42","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Ann Stat. https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann Stat"},{"issue":"1","key":"2811_CR43","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"2811_CR44","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1022643204877","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81\u2013106","journal-title":"Mach Learn"},{"key":"2811_CR45","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 45:5\u201332","journal-title":"Mach Learn"},{"key":"2811_CR46","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1018054314350","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24:123\u2013140","journal-title":"Mach Learn"},{"key":"2811_CR47","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel, V, Thirion, B, Grisel, O, & Duchesnay, \u00c9 (2011) Scikit-learn: Machine learning in Python. J mach Learn res 12: 2825\u20132830"},{"issue":"1","key":"2811_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):1\u201313","journal-title":"BMC Genomics"},{"issue":"3","key":"2811_CR49","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TKDE.2005.50","volume":"17","author":"J Huang","year":"2005","unstructured":"Huang J, Ling CX (2005) Using AUC and accuracy in evaluating learning algorithms. Ieee Trans Knowl Data Eng 17(3):299\u2013310","journal-title":"Ieee Trans Knowl Data Eng"},{"key":"2811_CR50","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, & Bowyer KW (2003) SMOTEBoost: Improving prediction of the minority class in boosting. In: Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22\u201326, 2003. Proceedings 7 (pp. 107\u2013119). Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-540-39804-2_12"},{"issue":"2","key":"2811_CR51","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1007\/s10994-023-06427-5","volume":"113","author":"VA Huynh-Thu","year":"2024","unstructured":"Huynh-Thu VA, Geurts P (2024) Optimizing model-agnostic random subspace ensembles. Mach Learn 113(2):993\u20131042","journal-title":"Mach Learn"},{"key":"2811_CR52","doi-asserted-by":"crossref","unstructured":"Liu Z, Cao W, Gao Z, Bian J, Chen H, Chang Y, & Liu TY (2020, April) Self-paced ensemble for highly imbalanced massive data classification. In: 2020 IEEE 36th international conference on data engineering (ICDE) (pp. 841\u2013852). IEEE.","DOI":"10.1109\/ICDE48307.2020.00078"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02811-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02811-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02811-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:01:37Z","timestamp":1780761697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02811-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,6]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2811"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02811-4","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,6]]},"assertion":[{"value":"21 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2026","order":4,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"177"}}