{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:15:04Z","timestamp":1778688904887,"version":"3.51.4"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["No.2023YFB3308600"],"award-info":[{"award-number":["No.2023YFB3308600"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-025-02894-5","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:41:59Z","timestamp":1773657719000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Model for service quality evaluation in multi-value chain networks using causality and multiple classifier system"],"prefix":"10.1007","volume":"17","author":[{"given":"Jingxiong","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linfu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"2894_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106590","volume":"212","author":"C Xu","year":"2021","unstructured":"Xu C, Fu C, Liu W, Sheng S, Yang S (2021) Data-driven decision model based on dynamical classifier selection. Knowl-Based Syst 212:106590. https:\/\/doi.org\/10.1016\/j.knosys.2020.106590","journal-title":"Knowl-Based Syst"},{"issue":"37","key":"2894_CR2","doi-asserted-by":"publisher","first-page":"7120","DOI":"10.1126\/scirobotics.aay7120","volume":"4","author":"D Gunning","year":"2019","unstructured":"Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang G-Z (2019) XAI\u2013Explainable artificial intelligence. Sci Robot 4(37):7120. https:\/\/doi.org\/10.1126\/scirobotics.aay7120","journal-title":"Sci Robot"},{"key":"2894_CR3","doi-asserted-by":"publisher","unstructured":"Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C (2021) Interpretable machine learning: fundamental principles and 10 grand challenges. https:\/\/doi.org\/10.48550\/ARXIV.2103.11251. Publisher: arXiv Version Number 2. Accessed 21 Dec 2023","DOI":"10.48550\/ARXIV.2103.11251"},{"key":"2894_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110729","volume":"146","author":"W Messner","year":"2023","unstructured":"Messner W (2023) From black box to clear box: a hypothesis testing framework for scalar regression problems using deep artificial neural networks. Appl Soft Comput 146:110729. https:\/\/doi.org\/10.1016\/j.asoc.2023.110729","journal-title":"Appl Soft Comput"},{"key":"2894_CR5","doi-asserted-by":"crossref","unstructured":"Liu P, Sun L (2022) Multi-service value chains collaboration for repairperson resources selection using a many-objective evolutionary algorithm with adaptive reference vectors. Appl Soft Comput 131:109771. Publisher: Elsevier","DOI":"10.1016\/j.asoc.2022.109771"},{"key":"2894_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-1870-3","volume-title":"Intuitionistic Fuzzy Sets","author":"KT Atanassov","year":"1999","unstructured":"Atanassov KT, Atanassov KT (1999) Intuitionistic Fuzzy Sets. Springer, Berlin"},{"key":"2894_CR7","doi-asserted-by":"crossref","unstructured":"Atanassov KT, Atanassov KT (1999) Interval valued intuitionistic fuzzy sets. Intuit Fuzzy Sets Theory Appl, pp 139\u2013177","DOI":"10.1007\/978-3-7908-1870-3_2"},{"key":"2894_CR8","doi-asserted-by":"crossref","unstructured":"Yager RR (2013) Pythagorean fuzzy subsets. In: 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA\/NAFIPS). IEEE, pp 57\u201361","DOI":"10.1109\/IFSA-NAFIPS.2013.6608375"},{"key":"2894_CR9","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s00500-020-05193-z","volume":"25","author":"L Wang","year":"2021","unstructured":"Wang L, Garg H, Li N (2021) Pythagorean fuzzy interactive Hamacher power aggregation operators for assessment of express service quality with entropy weight. Soft Comput 25:973\u2013993","journal-title":"Soft Comput"},{"key":"2894_CR10","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s12652-019-01377-0","volume":"11","author":"T Senapati","year":"2020","unstructured":"Senapati T, Yager RR (2020) Fermatean fuzzy sets. J Ambient Intell Humaniz Comput 11:663\u2013674","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2894_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115613","volume":"185","author":"S Jeevaraj","year":"2021","unstructured":"Jeevaraj S (2021) Ordering of interval-valued Fermatean fuzzy sets and its applications. Expert Syst Appl 185:115613. https:\/\/doi.org\/10.1016\/j.eswa.2021.115613","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2894_CR12","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/879629","volume":"2012","author":"B Zhu","year":"2012","unstructured":"Zhu B, Xu Z, Xia M (2012) Dual hesitant fuzzy sets. J Appl Math 2012(1):879629","journal-title":"J Appl Math"},{"issue":"5","key":"2894_CR13","doi-asserted-by":"publisher","first-page":"5225","DOI":"10.3233\/JIFS-169806","volume":"35","author":"BP Joshi","year":"2018","unstructured":"Joshi BP, Singh A, Bhatt PK, Vaisla KS (2018) Interval valued q-rung orthopair fuzzy sets and their properties. J Intell Fuzzy Syst 35(5):5225\u20135230. https:\/\/doi.org\/10.3233\/JIFS-169806","journal-title":"J Intell Fuzzy Syst"},{"issue":"2","key":"2894_CR14","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1080\/18756891.2010.9727692","volume":"3","author":"H Bustince","year":"2010","unstructured":"Bustince H (2010) Interval-valued fuzzy sets in soft computing. Int J Comput Intell Syst 3(2):215\u2013222. https:\/\/doi.org\/10.1080\/18756891.2010.9727692","journal-title":"Int J Comput Intell Syst"},{"key":"2894_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107578","volume":"129","author":"RM Zulqarnain","year":"2024","unstructured":"Zulqarnain RM, Garg H, Ma W-X, Siddique I (2024) Optimal cloud service provider selection: an MADM framework on correlation-based TOPSIS with interval-valued q-rung orthopair fuzzy soft set. Eng Appl Artif Intell 129:107578. https:\/\/doi.org\/10.1016\/j.engappai.2023.107578","journal-title":"Eng Appl Artif Intell"},{"key":"2894_CR16","doi-asserted-by":"publisher","first-page":"55726","DOI":"10.1109\/ACCESS.2024.3384874","volume":"12","author":"S Korucuk","year":"2024","unstructured":"Korucuk S, Aytek\u0131n A, Moslem S (2024) A novel interval-valued-q-rung orthopair fuzzy-additive ratio assessment model for evaluating logistics service quality. IEEE Access 12:55726\u201355743. https:\/\/doi.org\/10.1109\/ACCESS.2024.3384874","journal-title":"IEEE Access"},{"key":"2894_CR17","doi-asserted-by":"publisher","unstructured":"Kutlu\u00a0G\u00fcndo\u011fdu F, Kahraman C (2021) Hospital performance assessment using interval-valued spherical fuzzy analytic hierarchy process. Kahraman C, Kutlu\u00a0G\u00fcndo\u011fdu F (eds) Decision making with spherical fuzzy sets. Springer, Cham vol. 392, pp 349\u2013373. https:\/\/doi.org\/10.1007\/978-3-030-45461-6_15. Series title: studies in fuzziness and soft computing. http:\/\/link.springer.com\/10.1007\/978-3-030-45461-6_15. Accessed 01 Aug 2024","DOI":"10.1007\/978-3-030-45461-6_15"},{"key":"2894_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115757","volume":"186","author":"E Tumsekcali","year":"2021","unstructured":"Tumsekcali E, Ayyildiz E, Taskin A (2021) Interval valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL Model: P-SERVQUAL 4.0. Expert Syst Appl 186:115757. https:\/\/doi.org\/10.1016\/j.eswa.2021.115757","journal-title":"Expert Syst Appl"},{"issue":"7","key":"2894_CR19","doi-asserted-by":"publisher","first-page":"991","DOI":"10.3390\/jmse10070991","volume":"10","author":"Y-J Wang","year":"2022","unstructured":"Wang Y-J, Liu L-J, Han T-C (2022) Interval-valued fuzzy multi-criteria decision-making with dependent evaluation criteria for evaluating service performance of international container ports. J Mar Sci Eng 10(7):991. https:\/\/doi.org\/10.3390\/jmse10070991","journal-title":"J Mar Sci Eng"},{"key":"2894_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122248","volume":"238","author":"D Zhang","year":"2024","unstructured":"Zhang D, Hu J (2024) A novel multi-interval-valued fuzzy set model to solve MADM problems. Expert Syst Appl 238:122248. https:\/\/doi.org\/10.1016\/j.eswa.2023.122248","journal-title":"Expert Syst Appl"},{"key":"2894_CR21","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1016\/j.ins.2023.01.070","volume":"626","author":"N Chai","year":"2023","unstructured":"Chai N, Zhou W, Jiang Z (2023) Sustainable supplier selection using an intuitionistic and interval-valued fuzzy MCDM approach based on cumulative prospect theory. Inf Sci 626:710\u2013737. https:\/\/doi.org\/10.1016\/j.ins.2023.01.070","journal-title":"Inf Sci"},{"key":"2894_CR22","doi-asserted-by":"publisher","unstructured":"Alhussan A, Khafaga D, El-kenawy E-S, Ibrahim A, Eid M, Abdelhamid A (2022) Pothole and plain road classification using adaptive mutation dipper throated optimization and transfer learning for self driving cars. IEEE Access, 1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2022.3196660","DOI":"10.1109\/ACCESS.2022.3196660"},{"key":"2894_CR23","doi-asserted-by":"publisher","first-page":"3749","DOI":"10.32604\/cmc.2021.018179","volume":"69","author":"A Salamai","year":"2021","unstructured":"Salamai A, El-kenawy E-S, Ibrahim A (2021) Dynamic voting classifier for risk identification in supply chain 4.0. Comput Mater Contin 69:3749\u20133766. https:\/\/doi.org\/10.32604\/cmc.2021.018179","journal-title":"Comput Mater Contin"},{"key":"2894_CR24","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.inffus.2017.09.010","volume":"41","author":"RMO Cruz","year":"2018","unstructured":"Cruz RMO, Sabourin R, Cavalcanti GDC (2018) Dynamic classifier selection: recent advances and perspectives. Inf Fusion 41:195\u2013216. https:\/\/doi.org\/10.1016\/j.inffus.2017.09.010","journal-title":"Inf Fusion"},{"key":"2894_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2955983","author":"E Hassib","year":"2019","unstructured":"Hassib E, El-Desouky A, El-kenawy E-S, Elghamrawy S (2019) An imbalanced big data mining framework for improving optimization algorithms performance. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2955983","journal-title":"IEEE Access"},{"key":"2894_CR26","doi-asserted-by":"crossref","unstructured":"Pathan S, Siddalingaswamy P, Kumar P, MM MP, Ali T, Acharya UR (2021) Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images. Comput Biol Med 137:104835. Publisher: Elsevier","DOI":"10.1016\/j.compbiomed.2021.104835"},{"key":"2894_CR27","doi-asserted-by":"crossref","unstructured":"Junior LM, Nardini FM, Renso C, Trani R, Macedo JA (2020) A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems. Expert Syst Appl 152:113351. Publisher: Elsevier","DOI":"10.1016\/j.eswa.2020.113351"},{"key":"2894_CR28","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.inffus.2020.09.004","volume":"66","author":"P Zyblewski","year":"2021","unstructured":"Zyblewski P, Sabourin R, Wo\u017aniak M (2021) Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams. Inf Fusion 66:138\u2013154","journal-title":"Inf Fusion"},{"issue":"2","key":"2894_CR29","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1109\/TBIOM.2019.2949364","volume":"2","author":"F Mokhayeri","year":"2019","unstructured":"Mokhayeri F, Granger E (2019) Video face recognition using siamese networks with block-sparsity matching. IEEE Trans Biom Behav Identity Sci 2(2):133\u2013144","journal-title":"IEEE Trans Biom Behav Identity Sci"},{"key":"2894_CR30","doi-asserted-by":"crossref","unstructured":"Vit\u00f3rio JGB, Junior ASB, MG CY, Silla CN (2023) Multi-modal music mood classification with dynamic classifier selection. IEEE, pp 1\u20135","DOI":"10.1109\/IWSSIP58668.2023.10180235"},{"key":"2894_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102036","volume":"102","author":"R Davtalab","year":"2024","unstructured":"Davtalab R, Cruz RM, Sabourin R (2024) A scalable dynamic ensemble selection using fuzzy hyperboxes. Inf Fusion 102:102036","journal-title":"Inf Fusion"},{"key":"2894_CR32","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.patcog.2018.07.037","volume":"85","author":"RMO Cruz","year":"2019","unstructured":"Cruz RMO, Oliveira DVR, Cavalcanti GDC, Sabourin R (2019) FIRE-DES++: enhanced online pruning of base classifiers for dynamic ensemble selection. Pattern Recogn 85:149\u2013160","journal-title":"Pattern Recogn"},{"issue":"10\u201311","key":"2894_CR33","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1016\/j.patcog.2011.03.020","volume":"44","author":"T Woloszynski","year":"2011","unstructured":"Woloszynski T, Kurzynski M (2011) A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognit 44(10\u201311):2656\u20132668","journal-title":"Pattern Recognit"},{"issue":"5","key":"2894_CR34","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1016\/j.patcog.2014.12.003","volume":"48","author":"RM Cruz","year":"2015","unstructured":"Cruz RM, Sabourin R, Cavalcanti GD, Ren TI (2015) META-DES: a dynamic ensemble selection framework using meta-learning. Pattern Recognit 48(5):1925\u20131935","journal-title":"Pattern Recognit"},{"key":"2894_CR35","doi-asserted-by":"publisher","first-page":"12241","DOI":"10.1007\/s00500-020-04668-3","volume":"24","author":"J Elmi","year":"2020","unstructured":"Elmi J, Eftekhari M (2020) Dynamic ensemble selection based on hesitant fuzzy multiple criteria decision making. Soft Comput 24:12241\u201312253","journal-title":"Soft Comput"},{"key":"2894_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107257","volume":"104","author":"J Elmi","year":"2021","unstructured":"Elmi J, Eftekhari M (2021) Multi-Layer Selector(MLS): dynamic selection based on filtering some competence measures. Appl Soft Comput 104:107257. https:\/\/doi.org\/10.1016\/j.asoc.2021.107257","journal-title":"Appl Soft Comput"},{"key":"2894_CR37","doi-asserted-by":"crossref","unstructured":"Elmi J, Eftekhari M, Mehrpooya A, Ravari MR (2023) A novel framework based on the multi-label classification for dynamic selection of classifiers. Int J Mach Learn Cybern, 1\u201318","DOI":"10.1007\/s13042-022-01751-z"},{"key":"2894_CR38","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.inffus.2022.09.010","volume":"90","author":"MA Souza","year":"2023","unstructured":"Souza MA, Sabourin R, Cavalcanti GDC, Cruz RMO (2023) OLP++: an online local classifier for high dimensional data. Inf Fusion 90:120\u2013137. https:\/\/doi.org\/10.1016\/j.inffus.2022.09.010","journal-title":"Inf Fusion"},{"issue":"12","key":"2894_CR39","doi-asserted-by":"publisher","first-page":"3408","DOI":"10.13196\/j.cims.2020.12.023","volume":"26","author":"P Liu","year":"2020","unstructured":"Liu P, Sun L, Zhang C (2020) Evaluation of service station based on combined weight and nested ensemble classifier. Comput Integrat Manufact Syst 26(12):3408\u20133426. https:\/\/doi.org\/10.13196\/j.cims.2020.12.023","journal-title":"Comput Integrat Manufact Syst"},{"key":"2894_CR40","doi-asserted-by":"crossref","unstructured":"Brand JE, Zhou X, Xie Y (2023) Recent Developments in Causal Inference and Machine Learning. Annu Rev Sociol 49. Publisher: Annual Reviews","DOI":"10.1146\/annurev-soc-030420-015345"},{"issue":"5","key":"2894_CR41","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s40264-022-01155-6","volume":"45","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Yu Y, Wang H, Li Y, Deng Y, Jiang G, Luo Y (2022) Machine learning in causal inference: application in pharmacovigilance. Drug Safety 45(5):459\u2013476","journal-title":"Drug Safety"},{"issue":"1","key":"2894_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41937-023-00113-y","volume":"159","author":"M Lechner","year":"2023","unstructured":"Lechner M (2023) Causal machine learning and its use for public policy. Swiss J Econ Stat 159(1):1\u201315","journal-title":"Swiss J Econ Stat"},{"issue":"1","key":"2894_CR43","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1145\/3400051.3400058","volume":"22","author":"R Moraffah","year":"2020","unstructured":"Moraffah R, Karami M, Guo R, Raglin A, Liu H (2020) Causal interpretability for machine learning-problems, methods and evaluation. ACM SIGKDD Explor Newsl 22(1):18\u201333","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"6","key":"2894_CR44","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1109\/TAI.2022.3150264","volume":"3","author":"L Cheng","year":"2022","unstructured":"Cheng L, Guo R, Moraffah R, Sheth P, Candan KS, Liu H (2022) Evaluation methods and measures for causal learning algorithms. IEEE Trans Artif Intell 3(6):924\u2013943. https:\/\/doi.org\/10.1109\/TAI.2022.3150264","journal-title":"IEEE Trans Artif Intell"},{"key":"2894_CR45","unstructured":"Schut L, Key O, Mcgrath R, Costabello L, Gal Y (2021) Generating interpretable counterfactual explanations by implicit minimisation of epistemic and aleatoric uncertainties"},{"key":"2894_CR46","unstructured":"Mahajan D, Tan C, Sharma A (2019) Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277"},{"key":"2894_CR47","doi-asserted-by":"crossref","unstructured":"Laugel T, Jeyasothy A, Lesot M-J, Marsala C, Detyniecki M (2023) Achieving diversity in counterfactual explanations: a review and discussion, pp 1859\u20131869","DOI":"10.1145\/3593013.3594122"},{"key":"2894_CR48","unstructured":"Schwab P, Karlen W (2019) Cxplain: Causal explanations for model interpretation under uncertainty. Adv Neural Inf Process Syst 32"},{"key":"2894_CR49","first-page":"5453","volume":"33","author":"M O\u2019Shaughnessy","year":"2020","unstructured":"O\u2019Shaughnessy M, Canal G, Connor M, Rozell C, Davenport M (2020) Generative causal explanations of black-box classifiers. Adv Neural Inf Process Syst 33:5453\u20135467","journal-title":"Adv Neural Inf Process Syst"},{"key":"2894_CR50","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30"},{"key":"2894_CR51","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.neucom.2017.05.084","volume":"266","author":"FDA Carvalho","year":"2017","unstructured":"Carvalho FDA, Sim\u00f5es EC (2017) Fuzzy clustering of interval-valued data with City-Block and Hausdorff distances. Neurocomputing 266:659\u2013673","journal-title":"Neurocomputing"},{"key":"2894_CR52","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.rcim.2017.11.012","volume":"51","author":"J Zhang","year":"2018","unstructured":"Zhang J, Pang J, Yu J, Wang P (2018) An efficient assembly retrieval method based on Hausdorff distance. Robot Comput-Integr Manuf 51:103\u2013111. https:\/\/doi.org\/10.1016\/j.rcim.2017.11.012","journal-title":"Robot Comput-Integr Manuf"},{"key":"2894_CR53","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.ins.2016.08.068","volume":"372","author":"AB Ramos-Guajardo","year":"2016","unstructured":"Ramos-Guajardo AB, Grzegorzewski P (2016) Distance-based linear discriminant analysis for interval-valued data. Inf Sci 372:591\u2013607. https:\/\/doi.org\/10.1016\/j.ins.2016.08.068","journal-title":"Inf Sci"},{"issue":"11","key":"2894_CR54","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1016\/j.patrec.2008.04.008","volume":"29","author":"A Irpino","year":"2008","unstructured":"Irpino A, Verde R (2008) Dynamic clustering of interval data using a Wasserstein-based distance. Pattern Recognit Lett 29(11):1648\u20131658","journal-title":"Pattern Recognit Lett"},{"key":"2894_CR55","doi-asserted-by":"crossref","unstructured":"Bertossi L, Li J, Schleich M, Suciu D, Vagena Z (2020) Causality-based explanation of classification outcomes, pp 1\u201310","DOI":"10.1145\/3399579.3399865"},{"issue":"11","key":"2894_CR56","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TFUZZ.2019.2945239","volume":"28","author":"X Liu","year":"2019","unstructured":"Liu X, Jia W, Liu W, Pedrycz W (2019) Afsse: an interpretable classifier with axiomatic fuzzy set and semantic entropy. IEEE Trans Fuzzy Syst 28(11):2825\u20132840","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"6","key":"2894_CR57","doi-asserted-by":"publisher","first-page":"4661","DOI":"10.1109\/TCYB.2020.3032707","volume":"52","author":"W Jia","year":"2020","unstructured":"Jia W, Liu X, Wang Y, Pedrycz W, Zhou J (2020) Semisupervised learning via axiomatic fuzzy set theory and svm. IEEE Trans Cybern 52(6):4661\u20134674","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"2894_CR58","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TGRS.2002.1006354","volume":"40","author":"PC Smits","year":"2002","unstructured":"Smits PC (2002) Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection. IEEE Trans Geosci Remote Sens 40(4):801\u2013813","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"200","key":"2894_CR59","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701","journal-title":"J Am Stat Assoc"},{"key":"2894_CR60","unstructured":"Chen C, Lin K, Rudin C, Shaposhnik Y, Wang S, Wang T (2018) An interpretable model with globally consistent explanations for credit risk. arXiv preprint arXiv:1811.12615"},{"key":"2894_CR61","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201cWhy Should I Trust You?\u201d: Explaining the predictions of any classifier. arXiv:1602.04938","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"2894_CR62","doi-asserted-by":"publisher","first-page":"6942","DOI":"10.46298\/lmcs-17(3:22)2021","volume":"17","author":"E Livshits","year":"2021","unstructured":"Livshits E, Bertossi L, Kimelfeld B, Sebag M (2021) The Shapley value of tuples in query answering. Log Methods Comput Sci 17(3):6942. https:\/\/doi.org\/10.46298\/lmcs-17(3:22)2021","journal-title":"Log Methods Comput Sci"},{"key":"2894_CR63","unstructured":"Sun L, others, University SJ, et al (2023) Manufacturing multi-value chain distributed data space and its management and operation service platform. Science and Technology Achievement Appraisal Certificate JK Authentication No. 2124, China Machinery Industry Federation (2023). Appraisal chaired by Acad. Yang Huayong, Acad. Yu Haibin, etc"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02894-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02894-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02894-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:02:32Z","timestamp":1778688152000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02894-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,16]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2894"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02894-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,16]]},"assertion":[{"value":"12 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"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":"193"}}