{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:13:01Z","timestamp":1759482781963,"version":"3.40.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976108","61572241"],"award-info":[{"award-number":["61976108","61572241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["22YJC870001"],"award-info":[{"award-number":["22YJC870001"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s40747-025-01854-4","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T10:22:59Z","timestamp":1745317379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Encoding local label correlations in multi-instance multi-label learning with an improved multi-objective particle swarm optimization"],"prefix":"10.1007","volume":"11","author":[{"given":"Xiang","family":"Bao","sequence":"first","affiliation":[]},{"given":"Fei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Qinghua","family":"Ling","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"issue":"3","key":"1854_CR1","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1109\/TPAMI.2020.3017456","volume":"44","author":"T Nguyen","year":"2022","unstructured":"Nguyen T, Raich R (2022) Incomplete label multiple instance multiple label learning. IEEE Trans Pattern Anal Mach Intell 44(3):1320\u20131337","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"1854_CR2","first-page":"1","volume":"43","author":"F Briggs","year":"2011","unstructured":"Briggs F, Fern X-Z, Raich R (2011) Context-aware MIML instance annotation: exploiting label correlations with classifier chains. Knowl Inf Syst 43(1):1\u201327","journal-title":"Knowl Inf Syst"},{"key":"1854_CR3","doi-asserted-by":"publisher","first-page":"109899","DOI":"10.1109\/ACCESS.2019.2928218","volume":"7","author":"H Hu","year":"2019","unstructured":"Hu H, Cui Z, Wu J, Wang K (2019) Metric learning based multi-instance multi-label classification with label correlation. IEEE Access 7:109899\u2013109909","journal-title":"IEEE Access"},{"key":"1854_CR4","unstructured":"Li Y-F, Hu J-A, Jiang Y, Zhou Z-H (2012) Towards discovering what patterns trigger what labels. In: Proceedings of the AAAI conference on artificial intelligence"},{"key":"1854_CR5","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.asoc.2015.05.023","volume":"34","author":"M Abdechiri","year":"2015","unstructured":"Abdechiri M, Faez K (2015) Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks. Appl Soft Comput 34:788\u2013798","journal-title":"Appl Soft Comput"},{"key":"1854_CR6","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.ins.2022.11.153","volume":"622","author":"Y Gong","year":"2023","unstructured":"Gong Y, Wu Q, Zhou M, Wen J (2023) Self-paced multi-label co-training. Inf Sci 622:269\u2013281","journal-title":"Inf Sci"},{"key":"1854_CR7","doi-asserted-by":"crossref","unstructured":"Aggarwal A, Ghoshal S, Ankith MS, Sinha S, Ramakrishnan G, Kar P, Jain P (2017) Scalable optimization of multivariate performance measures in multi-instance multi-label learning. In: 31st AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10947"},{"issue":"3","key":"1854_CR8","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TEVC.2004.826067","volume":"8","author":"CAC Coello","year":"2004","unstructured":"Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256\u2013279","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"1854_CR9","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1016\/j.artint.2011.10.002","volume":"176","author":"Z-H Zhou","year":"2012","unstructured":"Zhou Z-H, Zhang M-L, Huang S-J, Li Y-F (2012) Multi-instance multi-label learning. Artif Intell 176(1):2291\u20132320","journal-title":"Artif Intell"},{"key":"1854_CR10","unstructured":"Yang S-H, Zha H, Hu B-G (2009) Dirichlet-bernoulli alignment: A generative model for multi-class multi-label multi-instance corpora. In: 23rd Annual conference on neural information processing systems"},{"key":"1854_CR11","unstructured":"Zha Z-J, Hua X-S, Mei T, Wang J-D, Qi G-J Wang Z-F (2008) Joint multi-label multi-instance learning for image classification. In: 2008 IEEE conference on computer vision and pattern recognition"},{"key":"1854_CR12","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1109\/TSP.2023.3242091","volume":"71","author":"Z Pan","year":"2023","unstructured":"Pan Z, Wang B, Zhang R, Li Y (2023) MIML-GAN: a GAN-based algorithm for multi-instance multi-label learning on overlapping signal waveform recognition. IEEE Trans Signal Process 71:859\u2013872","journal-title":"IEEE Trans Signal Process"},{"issue":"5","key":"1854_CR13","doi-asserted-by":"publisher","first-page":"4377","DOI":"10.1109\/TAES.2022.3160978","volume":"58","author":"Z-S Pan","year":"2022","unstructured":"Pan Z-S, Wang S-F, Li Y-J (2022) Residual attention-aided U-Net GAN and multi-instance multilabel classifier for automatic waveform recognition of overlapping LPI radar signals. IEEE Trans Aerosp Electron Syst 58(5):4377\u20134395","journal-title":"IEEE Trans Aerosp Electron Syst"},{"issue":"12","key":"1854_CR14","doi-asserted-by":"publisher","first-page":"7177","DOI":"10.1002\/int.22585","volume":"36","author":"C Su","year":"2021","unstructured":"Su C, Yan Z, Yu G (2021) Cost-effective multi-instance multilabel active learning. Int J Intell Syst 36(12):7177\u20137203","journal-title":"Int J Intell Syst"},{"key":"1854_CR15","doi-asserted-by":"crossref","unstructured":"Su G-L, Wu Z-Q, Zhou J (2023) Cost-effective multi-instance multilabel active learning via correlation of features. In: 30th IEEE international conference on image processing","DOI":"10.1109\/ICIP49359.2023.10222329"},{"issue":"9","key":"1854_CR16","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TNNLS.2021.3056436","volume":"33","author":"G-X Yu","year":"2022","unstructured":"Yu G-X, Xing Y-Y, Wang J, Domeniconi C, Zhang X-L (2022) Multiview Multi-Instance Multilabel Active Learning. IEEE Trans Neural Netw Learn Syst 33(9):4311\u20134321","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1854_CR17","doi-asserted-by":"crossref","unstructured":"Zhang M-L, Zhou Z-H (2008) M3MIML: a maximum margin method for multi-instance multi-label learning. In: Eighth IEEE international conference on data mining","DOI":"10.1109\/ICDM.2008.27"},{"key":"1854_CR18","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TPAMI.2018.2861732","volume":"41","author":"S-J Huang","year":"2019","unstructured":"Huang S-J, Gao W, Zhou Z-H (2019) Fast Multi-Instance Multi Label Learning. IEEE Trans Pattern Anal Mach Intell 41:2614\u20132627","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"16","key":"1854_CR19","doi-asserted-by":"publisher","first-page":"3951","DOI":"10.1016\/j.neucom.2009.07.008","volume":"72","author":"M-L Zhang","year":"2009","unstructured":"Zhang M-L, Wang Z-J (2009) MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing 72(16):3951\u20133956","journal-title":"Neurocomputing"},{"key":"1854_CR20","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neucom.2012.08.001","volume":"99","author":"Z Chen","year":"2013","unstructured":"Chen Z, Chi Z, Fu H, Feng D (2013) Multi-instance multi-label image classification: a neural approach. Neurocomputing 99:298\u2013306","journal-title":"Neurocomputing"},{"issue":"12","key":"1854_CR21","doi-asserted-by":"publisher","first-page":"6025","DOI":"10.1109\/TIP.2018.2864920","volume":"27","author":"L Song","year":"2018","unstructured":"Song L, Liu J, Qian B, Sun M, Yang K, Sun M, Abbas S (2018) A deep multi-modal CNN for multi-instance multi-label image classification. IEEE Trans Image Process 27(12):6025\u20136038","journal-title":"IEEE Trans Image Process"},{"key":"1854_CR22","first-page":"226","volume":"1001","author":"Y-B Wang","year":"2019","unstructured":"Wang Y-B, Pei G-S, Cheng Y-S (2019) Ensemble regression kernel extreme learning machines for multi-instance multi-label learning. Commun Comput Inf Sci 1001:226\u2013239","journal-title":"Commun Comput Inf Sci"},{"issue":"3","key":"1854_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3771-6","volume":"67","author":"W Tang","year":"2024","unstructured":"Tang W, Zhang W-J, Zhang M-L (2024) Multi-instance partial-label learning: towards exploiting dual inexact supervision. Sci China Inf Sci 67(3):132103","journal-title":"Sci China Inf Sci"},{"key":"1854_CR24","unstructured":"Yang S-J, Jiang Y, Zhou Z-H (2013) Multi-instance multi-label learning with weak label. In: Proceedings of the twenty-third international joint conference on artificial intelligence"},{"key":"1854_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103452","volume":"101","author":"Z-Y Wu","year":"2025","unstructured":"Wu Z-Y, Guo W, Zhou W, Ye H-J, Jiang Y, Li H-X, Li H-X, Zhou Z-H (2025) Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge. Med Image Anal 101:103452","journal-title":"Med Image Anal"},{"issue":"1","key":"1854_CR26","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TETCI.2023.3287978","volume":"8","author":"Q Lai","year":"2024","unstructured":"Lai Q, Zhou J-H, Gan Y-F, Vong C-M, Chen CLP (2024) Single-stage broad multi-instance multi-label learning (BMIML) with diverse inter-correlations and its application to medical image classification. IEEE Trans Emerg Top Comput Intell 8(1):828\u2013839","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"1","key":"1854_CR27","doi-asserted-by":"publisher","first-page":"2295818","DOI":"10.1080\/09540091.2023.2295818","volume":"36","author":"Y-D Liu","year":"2024","unstructured":"Liu Y-D, Xu F, Zhao Y-S, Ma Z-C, Wang T-K, Zhang S-X, Tian Y-H (2024) Hierarchical multi-instance multi-label learning for Chinese patent text classification. Connect Sci 36(1):2295818","journal-title":"Connect Sci"},{"issue":"2","key":"1854_CR28","first-page":"696","volume":"33","author":"Y Yang","year":"2019","unstructured":"Yang Y, Fu Z-Y, Zhan D-C, Liu Z-B, Jiang Y (2019) Semi-supervised multi-modal multi-instance multi-label deep network with optimal transport. IEEE Trans Knowl Data Eng 33(2):696\u2013709","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1854_CR29","doi-asserted-by":"publisher","first-page":"1972","DOI":"10.1007\/s10489-020-01891-1","volume":"51","author":"S Hurtado","year":"2021","unstructured":"Hurtado S, Garc\u00eda-Nieto J, Navas-Delgado I, Nebro A, Aldana-Montes J (2021) Reconstruction of gene regulatory networks with multi-objective particle swarm optimizers. Appl Intell 51(4):1972\u20131991","journal-title":"Appl Intell"},{"key":"1854_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107695","volume":"111","author":"W Tan","year":"2021","unstructured":"Tan W, Yuan X, Huang G, Liu Z (2021) Low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle by multi-objective particle swarm optimization. Appl Soft Comput 111:107695","journal-title":"Appl Soft Comput"},{"key":"1854_CR31","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.ins.2022.12.079","volume":"625","author":"Y-X Li","year":"2023","unstructured":"Li Y-X, Zhang Y, Hu W (2023) adaptive multi-objective particle swarm optimization based on virtual Pareto front. Inf Sci 625:206\u2013236","journal-title":"Inf Sci"},{"issue":"8","key":"1854_CR32","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1007\/s11431-021-2018-x","volume":"65","author":"H-G Han","year":"2022","unstructured":"Han H-G, Zhang L-L, Ying H, Qiao J-F (2022) Adaptive candidate estimation-assisted multi-objective particle swarm optimization. Sci China Technol Sci 65(8):1685\u20131699","journal-title":"Sci China Technol Sci"},{"key":"1854_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106661","volume":"96","author":"L Li","year":"2020","unstructured":"Li L, Li G, Chang L (2020) A many-objective particle swarm optimization with grid dominance ranking and clustering. Appl Soft Comput 96:106661","journal-title":"Appl Soft Comput"},{"key":"1854_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104866","volume":"112","author":"Y Yang","year":"2022","unstructured":"Yang Y, Liao Q, Wang J, Wang Y (2022) Application of multi-objective particle swarm optimization based on short-term memory and K-means clustering in multi-modal multi-objective optimization. Eng Appl Artif Intell 112:104866","journal-title":"Eng Appl Artif Intell"},{"issue":"7","key":"1854_CR35","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.1109\/TCYB.2019.2949204","volume":"51","author":"B-L Wu","year":"2021","unstructured":"Wu B-L, Hu W, Hu J-J, Yen G-G (2021) Adaptive multiobjective particle swarm optimization based on evolutionary state estimation. IEEE Trans Cybern 51(7):3738\u20133751","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"1854_CR36","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evol Comput"},{"key":"1854_CR37","doi-asserted-by":"crossref","unstructured":"de Miranda PBC, Prud\u00eancio RBC, de Carvalho ACPLF, Soares C (2012) Combining a multi-objective optimization approach with meta-learning for SVM parameter selection. In: IEEE international conference on systems man and cybernetics conference proceedings","DOI":"10.1109\/ICSMC.2012.6378235"},{"key":"1854_CR38","doi-asserted-by":"crossref","unstructured":"Hoseinkhani F, Nasersharif B (2015) A feature transformation method based on multi objective particle swarm optimization for reducing support vector machine error. In: 2nd International conference on pattern recognition and image analysis IPRIA","DOI":"10.1109\/PRIA.2015.7161625"},{"key":"1854_CR39","doi-asserted-by":"crossref","unstructured":"Behravan I, Zahiri SH, Dehghantanha O (2016) An optimal SVM with feature selection using multi-objective PSO. In: 1st Conference on swarm intelligence and evolutionary computation (CSIEC)","DOI":"10.1155\/2016\/6305043"},{"key":"1854_CR40","doi-asserted-by":"crossref","unstructured":"Huang S-J, Zhou Z-H (2021) Multi-label learning by exploiting label correlations locally. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v26i1.8287"},{"issue":"6","key":"1854_CR41","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2013","unstructured":"Xue B, Zhang M-J, Browne W (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656\u20131671","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"1854_CR42","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1109\/TCBB.2014.2323058","volume":"11","author":"J-S Wu","year":"2014","unstructured":"Wu J-S, Huang S-J, Zhou Z-H (2014) Genome-wide protein function prediction through multi-instance multi-label learning. IEEE\/ACM Trans Comput Biol Bioinf 11(5):891\u2013902","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"1854_CR43","doi-asserted-by":"crossref","unstructured":"F. Briggs, X. Fern, R. Raich (2012) Rank-loss support instance machines for miml instance annotation. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2339530.2339616"},{"key":"1854_CR44","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"issue":"13","key":"1854_CR45","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248","journal-title":"Inf Sci"},{"key":"1854_CR46","doi-asserted-by":"crossref","unstructured":"Mozaffari M H, Abdy H, Zahiri S H (2013) Application of inclined planes system optimization on data clustering. In: Proceedings of the 1st Iranian Conference on pattern recognition and image analysis","DOI":"10.1109\/PRIA.2013.6528451"},{"key":"1854_CR47","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995\u2014international conference on neural networks"},{"key":"1854_CR48","unstructured":"Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength pareto evolutionary algorithm. Technical report Gloriastrasse"},{"key":"1854_CR49","unstructured":"Corne D, Jerram N R, Knowles J, Oates M J (2001) PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Proceedings of the genetic and evolutionary computation conference"},{"issue":"6","key":"1854_CR50","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang Q, Li H (2007) MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712\u2013731","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"1854_CR51","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577\u2013601","journal-title":"IEEE Trans Evol Comput"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01854-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01854-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01854-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T11:23:09Z","timestamp":1747480989000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01854-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":51,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1854"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01854-4","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"3 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2025","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"259"}}