{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T22:34:30Z","timestamp":1774305270829,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71972177"],"award-info":[{"award-number":["71972177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005784","name":"Dalarna University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005784","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&amp;BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&amp;BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.<\/jats:p>","DOI":"10.1007\/s40747-024-01528-7","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T05:01:22Z","timestamp":1721019682000},"page":"7373-7399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel bayesian network-based ensemble classifier chains for multi-label classification"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8620-4623","authenticated-orcid":false,"given":"Zhenwu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Shiqi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4212-8582","authenticated-orcid":false,"given":"Mengjie","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6643-2971","authenticated-orcid":false,"given":"Benting","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"issue":"01","key":"1528_CR1","first-page":"85","volume":"13","author":"NP Desai","year":"2022","unstructured":"Desai NP, Baluch MF, Makrariya A, Aziz RM (2022) Image processing model with deep learning approach for fish species classification. Turkish J Comput Math Educ 13(01):85\u201399","journal-title":"Turkish J Comput Math Educ"},{"key":"1528_CR2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"ES Robert","year":"2000","unstructured":"Robert ES, Yoram S (2000) BoosTexter: a boosting-based system for text categorization. Mach Learn 39:135\u2013168. https:\/\/doi.org\/10.1023\/A:1007649029923","journal-title":"Mach Learn"},{"key":"1528_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109723","volume":"239","author":"Y Tan","year":"2021","unstructured":"Tan Y, Zhang J, Tian H, Jiang D, Guo L, Wang G, Lin Y (2021) Multi-label classification for simultaneous fault diagnosis of marine machinery: a comparative study. Ocean Eng 239:109723","journal-title":"Ocean Eng"},{"issue":"182","key":"1528_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01525-7","volume":"21","author":"L Zhou","year":"2021","unstructured":"Zhou L, Zheng X, Yang D, Wang Y, Bai X, Ye X (2021) Application of multi-label classification models for the diagnosis of diabetic complications. BMC Med Inform Decis Mak 21(182):1\u201310. https:\/\/doi.org\/10.1186\/s12911-021-01525-7","journal-title":"BMC Med Inform Decis Mak"},{"key":"1528_CR5","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1109\/TCSVT.2018.2848458","volume":"29","author":"F Markatopoulou","year":"2019","unstructured":"Markatopoulou F, Mezaris V, Patras I (2019) Implicit and explicit concept relations in deep neural networks for multi-label video\/image annotation. IEEE Trans Circuits Syst Video Technol 29:1631\u20131644","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1528_CR6","doi-asserted-by":"crossref","unstructured":"Cetiner M, Akgul YS (2014) A Graphical Model Approach for Multi-Label Classification. In: Information Sciences and Systems 2014, Proceedings of the 29th International Symposium on Computer and Information Sciences, Krakow, Poland, pp: 61\u201367.","DOI":"10.1007\/978-3-319-09465-6_7"},{"key":"1528_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/e22101143","volume":"22","author":"Z Wang","year":"2020","unstructured":"Wang Z, Wang T, Wan B, Han M (2020) Partial classifier chains with feature selection by exploiting label correlation in multi-label classification. Entropy 22:1\u201322. https:\/\/doi.org\/10.3390\/e22101143","journal-title":"Entropy"},{"key":"1528_CR8","doi-asserted-by":"crossref","unstructured":"Read J, Pfahringer B, Holmes G, Frank E (2009) Classifier Chains for Multi-label Classification. In: Machine Learning and Knowledge Discovery in Databases, Buntine W, Grobelnik M, Mladeni\u0107 D, Shawe-Taylor J, editors, Springer: Berlin\/Heidelberg, Germany, pp: 254\u2013269","DOI":"10.1007\/978-3-642-04174-7_17"},{"key":"1528_CR9","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85:333\u2013359. https:\/\/doi.org\/10.1007\/s10994-011-5256-5","journal-title":"Mach Learn"},{"key":"1528_CR10","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patrec.2013.11.007","volume":"41","author":"EL Sucar","year":"2014","unstructured":"Sucar EL, Bielza C, Morales EF, Hernandez-Lea P, Zaragoza JH, Larra\u00f1aga P (2014) Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn Lett 41:14\u201322. https:\/\/doi.org\/10.1016\/j.patrec.2013.11.007","journal-title":"Pattern Recogn Lett"},{"key":"1528_CR11","first-page":"573","volume":"25","author":"B Fu","year":"2012","unstructured":"Fu B, Wang ZH (2012) A Multi-label classification method based on tree structure of label dependency. Pattern Recogn Artif Intellig 25:573\u2013580","journal-title":"Pattern Recogn Artif Intellig"},{"key":"1528_CR12","doi-asserted-by":"crossref","unstructured":"Chen B, Li W, Zhang Y, Hu J (2016) Enhancing multi-label classification based on local label constraints and classifier chains, in: Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver BC, Canada, 24\u201329 July pp:1458\u20131463","DOI":"10.1109\/IJCNN.2016.7727370"},{"key":"1528_CR13","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.ins.2016.02.037","volume":"351","author":"J Lee","year":"2016","unstructured":"Lee J, Kim H, Kim NR, Lee JH (2016) An approach for multi-label classification by directed acyclic graph with label correlation maximization. Inf Sci 351:101\u2013114. https:\/\/doi.org\/10.1016\/j.ins.2016.02.037","journal-title":"Inf Sci"},{"key":"1528_CR14","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.patrec.2013.11.007","volume":"22","author":"L Sun","year":"2018","unstructured":"Sun L, Kudo M (2018) Multi-label classification by polytree-augmented classifier chains with label-dependent features. Pattern Anal Appl 22:1029\u20131049. https:\/\/doi.org\/10.1016\/j.patrec.2013.11.007","journal-title":"Pattern Anal Appl"},{"key":"1528_CR15","doi-asserted-by":"crossref","unstructured":"Huang J, Li G, Wang S, Zhang W, Huang Q (2015) Group sensitive Classifier Chains for multi-label classification. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Turin, Italy, 29 June\u20133 July, pp: 1\u20136","DOI":"10.1109\/ICME.2015.7177400"},{"key":"1528_CR16","doi-asserted-by":"publisher","unstructured":"Kumar A, Vembu S, Menon AK, Elkan C (2012) Learning and inference in probabilistic classifier chains with beam search. In: Machine learning and knowledge discovery in databases, Flach PA, Bie TD, Cristianini N, editors, Springer: Berlin\/Heidelberg, Germany 7523:665\u2013680. https:\/\/doi.org\/10.1007\/978-3-642-33460-3_48","DOI":"10.1007\/978-3-642-33460-3_48"},{"key":"1528_CR17","first-page":"725","volume":"54","author":"L Chen","year":"2018","unstructured":"Chen L, Chen D (2018) A classifier chain method for multi-label learning based on kernel alignment. J Nanjing University (Nat Sci) 54:725\u2013732","journal-title":"J Nanjing University (Nat Sci)"},{"key":"1528_CR18","doi-asserted-by":"publisher","unstructured":"Read J, Martino L, Luengo D (2013) Efficient Monte Carlo optimization for multi-label classifier chains. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26\u201331 May, pp:3457\u20133461. https:\/\/doi.org\/10.1007\/978-3-642-33460-3_48","DOI":"10.1007\/978-3-642-33460-3_48"},{"key":"1528_CR19","doi-asserted-by":"publisher","unstructured":"Li N, Pan Z, Zhou X (2016) Classifier chain algorithm based on multi-label importance rank. Pattern Recogn Artif Intellig29:567\u2013575. https:\/\/doi.org\/10.16451\/j.cnki.issn1003-6059.201606011","DOI":"10.16451\/j.cnki.issn1003-6059.201606011"},{"key":"1528_CR20","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1016\/j.patcog.2015.01.004","volume":"48","author":"J Read","year":"2015","unstructured":"Read J, Martino L, Olmos PM, Luengo D (2015) Scalable multi-output label prediction: From classifier chains to classifier trellises. Pattern Recogn 48:2096\u20132109","journal-title":"Pattern Recogn"},{"key":"1528_CR21","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2019.01.039","volume":"335","author":"X Jun","year":"2019","unstructured":"Jun X, Lu Y, Lei Z, Guolun D (2019) Conditional entropy based classifier chains for multi-label classification. Neurocomputing 335:185\u2013194. https:\/\/doi.org\/10.1016\/j.neucom.2019.01.039","journal-title":"Neurocomputing"},{"key":"1528_CR22","doi-asserted-by":"crossref","unstructured":"Goncalves EC, Plastino A, Freitas AA (2013) A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains. In: Proceedings of 25th IEEE International Conference on Tools With Artificial Intelligence (ICTAI), Washington DC, USA, November, pp: 469\u2013476","DOI":"10.1109\/ICTAI.2013.76"},{"key":"1528_CR23","doi-asserted-by":"crossref","unstructured":"Eduardo CG, Plastino A, Freitas AA (2015) Simpler is Better: a Novel Genetic Algorithm to Induce Compact Multi-label Chain Classifiers. In: Proceedings of the 17th Genetic and Evolutionary Computation Conference (GECCO), Madrid, SAN MARINO, Spain, 11\u201315, July, pp: 559\u2013566","DOI":"10.1145\/2739480.2754650"},{"key":"1528_CR24","unstructured":"Dembczy\u0144sk K, Cheng W, H\u00fcllermeier E (2010) Bayes optimal multilabel classification via probabilistic classifier chain, in: Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 21\u201324 June, pp: 279\u2013286"},{"key":"1528_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1783-9","volume":"18","author":"M Riemenschneider","year":"2017","unstructured":"Riemenschneider M, Herbst A, Rasch A, Gorlatch S, Heider D (2017) eccCL: parallelized GPU implementation of ensemble classifier chains. BMC Bioinformatics 18:1\u20134. https:\/\/doi.org\/10.1186\/s12859-017-1783-9","journal-title":"BMC Bioinformatics"},{"key":"1528_CR26","doi-asserted-by":"crossref","unstructured":"Lin YA, Lin HT (2017) Cyclic Classifier Chain for Cost-Sensitive Multilabel Classification. In: Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, 19\u201321, October, pp: 11\u201320","DOI":"10.1109\/DSAA.2017.50"},{"key":"1528_CR27","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.ins.2020.12.010","volume":"554","author":"R Wang","year":"2021","unstructured":"Wang R, Ye S, Li K, Kwong S (2021) Bayesian network-based label correlation analysis for multi-label classifier chain. Inf Sci 554:256\u2013275. https:\/\/doi.org\/10.1016\/j.ins.2020.12.010","journal-title":"Inf Sci"},{"key":"1528_CR28","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2022.02.048","volume":"487","author":"S Moral-Garcia","year":"2022","unstructured":"Moral-Garcia S, Castellano JG, Mantas CJ, Abellan J (2022) A new label ordering method in Classifier Chains based on imprecise probabilities. Neurocomputing 487:34\u201345. https:\/\/doi.org\/10.1016\/j.neucom.2022.02.048","journal-title":"Neurocomputing"},{"key":"1528_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06162-3","author":"EL Mencia","year":"2021","unstructured":"Mencia EL, Kulessa M, Bohlender S, Furnkranz J (2021) Tree-based dynamic classifier chains. Mach Learn. https:\/\/doi.org\/10.1007\/s10994-022-06162-3","journal-title":"Mach Learn"},{"key":"1528_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2021.108395","volume":"117","author":"NK Mishra","year":"2022","unstructured":"Mishra NK, Singh PK (2022) Linear ordering problem based classifier chain using genetic algorithm for multi-label classification. Appl Soft Comput 117:1\u201315. https:\/\/doi.org\/10.1016\/j.asoc.2021.108395","journal-title":"Appl Soft Comput"},{"key":"1528_CR31","doi-asserted-by":"publisher","first-page":"51265","DOI":"10.1109\/ACCESS.2020.2980551","volume":"8","author":"W Weng","year":"2020","unstructured":"Weng W, Wang DH, Chen CL, Wen J, Wu SX (2020) Label specific features-based classifier chains for multi-label classification. IEEE Access 8:51265\u201351275. https:\/\/doi.org\/10.1109\/ACCESS.2020.2980551","journal-title":"IEEE Access"},{"issue":"3","key":"1528_CR32","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s10115-021-01647-4","volume":"64","author":"VF Rocha","year":"2022","unstructured":"Rocha VF, Varejao FM, Segatto MEV (2022) Ensemble of classifier chains and decision templates for multi-label classification. Knowl Inf Syst 64(3):643\u2013663. https:\/\/doi.org\/10.1007\/s10115-021-01647-4","journal-title":"Knowl Inf Syst"},{"key":"1528_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2020.106709","volume":"213","author":"P Teisseyre","year":"2021","unstructured":"Teisseyre P (2021) Classifier chains for positive unlabeled multi-label learning. Knowl-Based Syst 213:1\u201316. https:\/\/doi.org\/10.1016\/j.knosys.2020.106709","journal-title":"Knowl-Based Syst"},{"key":"1528_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2019.105292","volume":"192","author":"B Liu","year":"2020","unstructured":"Liu B, Tsoumakas G (2020) Dealing with class imbalance in classifier chains via random undersampling. Knowl-Based Syst 192:1\u201313. https:\/\/doi.org\/10.1016\/j.knosys.2019.105292","journal-title":"Knowl-Based Syst"},{"key":"1528_CR35","doi-asserted-by":"publisher","unstructured":"Zaki F, Afifi F, Gani A, Anuar NB (2022) Granular Network Traffic Classification for Streaming Traffic Using Incremental Learning and Classifier Chain. Malaysian J Comput Sci 35(3): 264\u2013280 https:\/\/doi.org\/10.22452\/mjcs.vol35no3.5","DOI":"10.22452\/mjcs.vol35no3.5"},{"key":"1528_CR36","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1111\/itor.13059","volume":"30","author":"S Radovanovic","year":"2023","unstructured":"Radovanovic S, Petrovic A, Delibasic B, Suknovic M (2023) A fair classifier chain for multi-label bank marketing strategy classification. Int Trans Oper Res 30:1320\u20131339. https:\/\/doi.org\/10.1111\/itor.13059","journal-title":"Int Trans Oper Res"},{"key":"1528_CR37","doi-asserted-by":"publisher","unstructured":"Zhang JH, Zhang ZH, Pu LR, Tang JJ, Guo F (2021) AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides. IEEE-ACM Transactions on Computational Biology and Bioinformatics. Proceedings of the 15th International Conference on Intelligent Computing, Aug 03\u201306, Nanchang, China, 18(5): 1831- 1840 https:\/\/doi.org\/10.1109\/TCBB.2020.2968419","DOI":"10.1109\/TCBB.2020.2968419"},{"key":"1528_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2021.104812","volume":"137","author":"YH Wang","year":"2021","unstructured":"Wang YH, Cai JY, Louie DC, Wang ZJ, Lee TK (2021) Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection. Comput Biol Med 137:1\u20139. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104812","journal-title":"Comput Biol Med"},{"key":"1528_CR39","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1016\/j.ins.2020.12.010","volume":"25","author":"YP Wu","year":"2022","unstructured":"Wu YP, Pei CH, Ruan CY, Wang RF, Yang Y, Zhang YC (2022) Bayesian network and chained classifiers based on SVM for traditional Chinese medical prescription generation. World Wide Web 25:1447\u20131468. https:\/\/doi.org\/10.1016\/j.ins.2020.12.010","journal-title":"World Wide Web"},{"issue":"1","key":"1528_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/genes14010071","volume":"14","author":"A Raza","year":"2023","unstructured":"Raza A, Rustam F, Siddiqui HUR, Diez ID, Garcia-Zapirain B, Lee E, Ashraf I (2023) Predicting genetic disorder and types of disorder using chain classifier approach. Genes 14(1):1\u201331. https:\/\/doi.org\/10.3390\/genes14010071","journal-title":"Genes"},{"issue":"1","key":"1528_CR41","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/TCBB.2022.3148577","volume":"20","author":"M Tharmakulasingam","year":"2023","unstructured":"Tharmakulasingam M, Gardner B, La Ragione R, Fernando A (2023) Rectified classifier chains for prediction of antibiotic resistance from multi-labelled data with missing labels. IEEE\/ACM Trans Comput Biol Bioinf 20(1):625\u2013636. https:\/\/doi.org\/10.1109\/TCBB.2022.3148577","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"1528_CR42","doi-asserted-by":"crossref","unstructured":"Zhou ZH (2012) Ensemble Methods Foundations and Algorithms. Chapman & Hall Press","DOI":"10.1201\/b12207"},{"issue":"6","key":"1528_CR43","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1089\/cmb.2021.0410","volume":"29","author":"RM Aziz","year":"2022","unstructured":"Aziz RM (2022) Cuckoo search-based optimization for cancer classification: a new hybrid approach. J Comput Biol 29(6):565\u2013584. https:\/\/doi.org\/10.1089\/cmb.2021.0410","journal-title":"J Comput Biol"},{"key":"1528_CR44","doi-asserted-by":"publisher","first-page":"6676","DOI":"10.1016\/j.apm.2016.02.014","volume":"40","author":"V Stojanovic","year":"2016","unstructured":"Stojanovic V, Nedic N, Prsic D, Dubonjic L (2016) Optimal experiment design for identification of ARX models with constrained output in non-Gaussian noise. Appl Math Model 40:6676\u20136689. https:\/\/doi.org\/10.1016\/j.apm.2016.02.014","journal-title":"Appl Math Model"},{"issue":"1","key":"1528_CR45","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/s10957-015-0706-z","volume":"168","author":"V Stojanovic","year":"2016","unstructured":"Stojanovic V, Nedic N (2016) A nature inspired parameter tuning approach to cascade control for hydraulically driven parallel robot platform. J Optim Theory Appl 168(1):332\u2013347. https:\/\/doi.org\/10.1007\/s10957-015-0706-z","journal-title":"J Optim Theory Appl"},{"issue":"11","key":"1528_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1361-6501\/ac8368","volume":"33","author":"HF Tao","year":"2022","unstructured":"Tao HF, Cheng L, Qiu JE, Stojanovic V (2022) Few shot cross equipment fault diagnosis method based on parameter optimization and feature metric. Measur Sci Technol 33(11):1\u201317. https:\/\/doi.org\/10.1088\/1361-6501\/ac8368","journal-title":"Measur Sci Technol"},{"key":"1528_CR47","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"1528_CR48","doi-asserted-by":"publisher","unstructured":"Wang XH, Yuan JB, Hua S, Duan BJ (2020) Optimization of wheel reprofiling based on the improved NSGA-II. Complexity https:\/\/doi.org\/10.1109\/4235.996017","DOI":"10.1109\/4235.996017"},{"key":"1528_CR49","doi-asserted-by":"crossref","unstructured":"Spyromitros E, Tsoumakas G, Vlahavas I (2008) An Empirical study of lazy multilabel classification Algorithms, In: Proc. 5th Hellenic Conference on Artificial Intelligence, pp: 401\u2013406","DOI":"10.1007\/978-3-540-87881-0_40"},{"key":"1528_CR50","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang ML, Zhou ZH (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn 40:2038\u20132048. https:\/\/doi.org\/10.1016\/j.patcog.2006.12.019","journal-title":"Pattern Recogn"},{"key":"1528_CR51","doi-asserted-by":"publisher","unstructured":"Tsoumakas G, Vlahavas I (2007) Random k-labelsets: an ensemble method for multilabel classification. In: Proceedings of the 18th European conference on Machine Learning, Springer Berlin Heidelberg, pp: 406\u2013417. https:\/\/doi.org\/10.1007\/978-3-540-74958-5_38","DOI":"10.1007\/978-3-540-74958-5_38"},{"issue":"9","key":"1528_CR52","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757\u20131771. https:\/\/doi.org\/10.1016\/j.patcog.2004.03.009","journal-title":"Pattern Recogn"},{"issue":"10","key":"1528_CR53","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","volume":"18","author":"ML Zhang","year":"2006","unstructured":"Zhang ML, Zhou ZH (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338\u20131351. https:\/\/doi.org\/10.1109\/TKDE.2006.162","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1528_CR54","first-page":"53","volume":"21","author":"G Tsoumakas","year":"2008","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2008) Effective and efficient multilabel classification in domains with large number of labels. Ecml\/pkdd Workshop Mining Multidimens Data 21:53\u201359","journal-title":"Ecml\/pkdd Workshop Mining Multidimens Data"},{"key":"1528_CR55","doi-asserted-by":"publisher","unstructured":"Zhang ML, Zhang K (2010) Multi-label learning by exploiting label dependency. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining 999\u20131008.https:\/\/doi.org\/10.1145\/1835804.1835930","DOI":"10.1145\/1835804.1835930"},{"key":"1528_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10846-005-9016-2","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J, Schuurmans D (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330. https:\/\/doi.org\/10.1007\/s10846-005-9016-2","journal-title":"J Mach Learn Res"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01528-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01528-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01528-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T15:26:24Z","timestamp":1726327584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01528-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,15]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1528"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01528-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,15]]},"assertion":[{"value":"6 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","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 conflicts of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}