{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:46:17Z","timestamp":1783100777451,"version":"3.54.6"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106174, 62266035"],"award-info":[{"award-number":["62106174, 62266035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s13042-023-02039-6","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T13:02:07Z","timestamp":1703250127000},"page":"2427-2437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring and exploiting hierarchical structures for large-scale classification"],"prefix":"10.1007","volume":"15","author":[{"given":"Junyan","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4788-8655","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenglei","family":"Pei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"2039_CR1","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1007\/s13042-021-01336-2","volume":"12","author":"M Al-taezi","year":"2021","unstructured":"Al-taezi M, Zhu P, Hu Q et al (2021) Self-paced hierarchical metric learning (sphml). Int J Mach Learn Cybern 12:2529\u20132541","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"2039_CR2","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1007\/s10618-014-0382-x","volume":"29","author":"K Aris","year":"2015","unstructured":"Aris K, Ioannis P, Eric G et al (2015) Evaluation measures for hierarchical classification: a unified view and novel approaches. Data Min Knowl Discov 29(3):820\u2013865","journal-title":"Data Min Knowl Discov"},{"key":"2039_CR3","unstructured":"Bengio S, Weston J, Grangier D (2013) Label embedding trees for large multi-class tasks. In: Advances in Neural Information Processing Systems, pp 163\u2013171"},{"key":"2039_CR4","doi-asserted-by":"crossref","unstructured":"Bennett PN, Nguyen N (2009) Refined experts: improving classification in large taxonomies. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 11\u201318","DOI":"10.1145\/1571941.1571946"},{"key":"2039_CR5","first-page":"155","volume":"10","author":"VD Blondel","year":"2008","unstructured":"Blondel VD, Guillaume JL, Lambiotte R et al (2008) Fast unfolding of communities in large networks. J Stat Mech 10:155\u2013168","journal-title":"J Stat Mech"},{"key":"2039_CR6","doi-asserted-by":"crossref","unstructured":"Ceci M, Malerba D (2003) Hierarchical classification of html documents with webclassii. In: European Conference on Information Retrieval, pp 57\u201372","DOI":"10.1007\/3-540-36618-0_5"},{"issue":"1","key":"2039_CR7","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s10844-006-0003-2","volume":"28","author":"M Ceci","year":"2007","unstructured":"Ceci M, Malerba D (2007) Classifying web documents in a hierarchy of categories: a comprehensive study. J Intell Inf Syst 28(1):37\u201378","journal-title":"J Intell Inf Syst"},{"issue":"3","key":"2039_CR8","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"2039_CR9","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1007\/s13042-022-01524-8","volume":"13","author":"M Dabbu","year":"2022","unstructured":"Dabbu M, Karuppusamy L, Pulugu D et al (2022) Water atom search algorithm-based deep recurrent neural network for the big data classification based on spark architecture. Int J Mach Learn Cybern 13:2297\u20132312","journal-title":"Int J Mach Learn Cybern"},{"key":"2039_CR10","first-page":"302","volume":"1","author":"S D\u2019Alessio","year":"2000","unstructured":"D\u2019Alessio S, Murray K, Schiaffino R et al (2000) The effect of using hierarchical classifiers in text categorization. Content-Based Multimed Inf Access 1:302\u2013313","journal-title":"Content-Based Multimed Inf Access"},{"key":"2039_CR11","doi-asserted-by":"crossref","unstructured":"Dekel O, Keshet J, Singer Y (2004) Large margin hierarchical classification. In: International Conference on Machine Learning, pp 27\u201335","DOI":"10.1145\/1015330.1015374"},{"issue":"Jan","key":"2039_CR12","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1\u201330","journal-title":"J Mach Learn Res"},{"key":"2039_CR13","unstructured":"Deng J, Krause J, Berg AC, et\u00a0al (2012) Hedging your bets: optimizing accuracy-specificity trade-offs in large scale visual recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3450\u20133457"},{"issue":"10\u201311","key":"2039_CR14","doi-asserted-by":"publisher","first-page":"2436","DOI":"10.1016\/j.patcog.2011.03.026","volume":"44","author":"I Dimitrovski","year":"2011","unstructured":"Dimitrovski I, Kocev D, Loskovska S et al (2011) Hierarchical annotation of medical images. Pattern Recognit 44(10\u201311):2436\u20132449","journal-title":"Pattern Recognit"},{"issue":"5","key":"2039_CR15","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1016\/j.patcog.2012.10.029","volume":"46","author":"P Dong","year":"2013","unstructured":"Dong P, Mei K, Zheng N et al (2013) Training inter-related classifiers for automatic image classification and annotation. Pattern Recognit 46(5):1382\u20131395","journal-title":"Pattern Recognit"},{"key":"2039_CR16","doi-asserted-by":"crossref","unstructured":"Dumais S, Chen H (2000) Hierarchical classification of web content. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 256\u2013263","DOI":"10.1145\/345508.345593"},{"key":"2039_CR17","unstructured":"Fagni T, Sebastiani F (2007) On the selection of negative examples for hierarchical text categorization. In: Proceedings of the 3rd Language & Technology Conference, pp 24\u201328"},{"issue":"11","key":"2039_CR18","doi-asserted-by":"publisher","first-page":"4172","DOI":"10.1109\/TIP.2015.2457337","volume":"24","author":"J Fan","year":"2015","unstructured":"Fan J, Peng J, Gao L et al (2015) Hierarchical learning of tree classifiers for large-scale plant species identification. IEEE Trans Image Process 24(11):4172\u20134184","journal-title":"IEEE Trans Image Process"},{"key":"2039_CR19","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1007\/s13042-021-01338-0","volume":"12","author":"S Fu","year":"2021","unstructured":"Fu S, Wang G, Xu J (2021) hier2vec: interpretable multi-granular representation learning for hierarchy in social networks. Int J Mach Learn Cybern 12:2543\u20132557","journal-title":"Int J Mach Learn Cybern"},{"key":"2039_CR20","doi-asserted-by":"crossref","unstructured":"Griffin G, Perona P (2008) Learning and using taxonomies for fast visual categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587410"},{"key":"2039_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"2039_CR22","first-page":"4214","volume":"45","author":"H Huang","year":"2022","unstructured":"Huang H, Wang Y, Hu Q et al (2022) Class-specific semantic reconstruction for open set recognition. IEEE Trans Pattern Anal Mach Intell 45(4):4214\u20134228","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2039_CR23","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1007\/s13042-023-01783-z","volume":"14","author":"H Huang","year":"2023","unstructured":"Huang H, Wang Y, Hu Q (2023) Building hierarchical class structures for extreme multi-class learning. Int J Mach Learn Cybern 14:2575\u20132590","journal-title":"Int J Mach Learn Cybern"},{"key":"2039_CR24","doi-asserted-by":"crossref","unstructured":"Koerich AL, Kalva PR (2005) Unconstrained handwritten character recognition using metaclasses of characters. In: IEEE International Conference on Image Processing, pp 538\u2013542","DOI":"10.1109\/ICIP.2005.1530112"},{"key":"2039_CR25","doi-asserted-by":"crossref","unstructured":"Lee K, Lee K, Min K, et\u00a0al (2018) Hierarchical novelty detection for visual object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1034\u20131042","DOI":"10.1109\/CVPR.2018.00114"},{"key":"2039_CR26","doi-asserted-by":"crossref","unstructured":"Liu B, Sadeghi F, Tappen M, et\u00a0al (2013) Probabilistic label trees for efficient large scale image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 843\u2013850","DOI":"10.1109\/CVPR.2013.114"},{"key":"2039_CR27","volume-title":"Explorations in parallel distributed processing: a handbook of models, programs, and exercises","author":"JL McClelland","year":"1989","unstructured":"McClelland JL, Rumelhart DE (1989) Explorations in parallel distributed processing: a handbook of models, programs, and exercises. MIT Press, Boston"},{"key":"2039_CR28","doi-asserted-by":"publisher","first-page":"3225","DOI":"10.1007\/s13042-022-01590-y","volume":"13","author":"L Pan","year":"2022","unstructured":"Pan L, Wang S, Ding Y et al (2022) A universal emotion recognition method based on feature priority evaluation and classifier reinforcement. Int J Mach Learn Cybern 13:3225\u20133237","journal-title":"Int J Mach Learn Cybern"},{"issue":"9","key":"2039_CR29","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.1109\/TIP.2016.2615423","volume":"26","author":"Y Qu","year":"2017","unstructured":"Qu Y, Lin L, Shen F et al (2017) Joint hierarchical category structure learning and large-scale image classification. IEEE Trans Image Process 26(9):4331\u20134346","journal-title":"IEEE Trans Image Process"},{"key":"2039_CR30","unstructured":"Sun A, Lim EP (2001) Hierarchical text classification and evaluation. In: Proceedings IEEE International Conference on Data Mining, pp 521\u2013528"},{"key":"2039_CR31","doi-asserted-by":"crossref","unstructured":"Tai L, Paolo G, Liu M (2017) Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) pp 31\u201336","DOI":"10.1109\/IROS.2017.8202134"},{"issue":"6022","key":"2039_CR32","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1126\/science.1192788","volume":"331","author":"JB Tenenbaum","year":"2011","unstructured":"Tenenbaum JB, Kemp C, Griffiths TL et al (2011) How to grow a mind: statistics, structure, and abstraction. Science 331(6022):1279\u20131285","journal-title":"Science"},{"issue":"3","key":"2039_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas G, Katakis I (2007) Multi-label classification: an overview. Int J Data Warehous Min 3(3):1\u201313","journal-title":"Int J Data Warehous Min"},{"key":"2039_CR34","doi-asserted-by":"crossref","unstructured":"Wang Y, Hu Q, Zhou Y, et\u00a0al (2017) Local Bayes risk minimization based stopping strategy for hierarchical classification. In: IEEE International Conference on Data Mining, pp 515\u2013524","DOI":"10.1109\/ICDM.2017.61"},{"key":"2039_CR35","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.ins.2021.12.009","volume":"586","author":"Y Wang","year":"2022","unstructured":"Wang Y, Hu Q, Chen H et al (2022) Uncertainty instructed multi-granularity decision for large-scale hierarchical classification. Inf Sci 586:644\u2013661","journal-title":"Inf Sci"},{"key":"2039_CR36","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s13042-015-0478-7","volume":"8","author":"JH Zhai","year":"2017","unstructured":"Zhai JH, Zhang S, Wang C (2017) The classification of imbalanced large data sets based on mapreduce and ensemble of elm classifiers. Int J Mach Learn Cybern 8:1009\u20131017","journal-title":"Int J Mach Learn Cybern"},{"key":"2039_CR37","doi-asserted-by":"crossref","unstructured":"Zhang NL, Wang X, Chen P (2014) A study of recently discovered equalities about latent tree models using inverse edges. In: European Workshop on Probabilistic Graphical Models, pp 567\u2013580","DOI":"10.1007\/978-3-319-11433-0_37"},{"key":"2039_CR38","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1007\/s13042-021-01493-4","volume":"13","author":"X Zhang","year":"2022","unstructured":"Zhang X, hong Zhou Y, Tang X et al (2022) Three-way improved neighborhood entropies based on three-level granular structures. Int J Mach Learn Cybern 13:1861\u20131890","journal-title":"Int J Mach Learn Cybern"},{"key":"2039_CR39","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.ijar.2018.10.017","volume":"104","author":"H Zhao","year":"2019","unstructured":"Zhao H, Yu S (2019) Cost-sensitive feature selection via the $$l_{2,1}-$$norm. Int J Approx Reason 104:25\u201337","journal-title":"Int J Approx Reason"},{"key":"2039_CR40","doi-asserted-by":"crossref","unstructured":"Zhao H, Zhu P, Wang P, et\u00a0al (2017) Hierarchical feature selection with recursive regularization. In: International Joint Conference on Artificial Intelligence, pp 3483\u20133489","DOI":"10.24963\/ijcai.2017\/487"},{"issue":"7","key":"2039_CR41","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1109\/TKDE.2019.2960251","volume":"33","author":"H Zhao","year":"2019","unstructured":"Zhao H, Hu Q, Zhu P et al (2019) A recursive regularization based feature selection framework for hierarchical classification. IEEE Trans Knowl Data Eng 33(7):2833\u20132846","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"2039_CR42","doi-asserted-by":"publisher","first-page":"4740","DOI":"10.1109\/TIP.2018.2845118","volume":"27","author":"T Zhao","year":"2018","unstructured":"Zhao T, Zhang B, He M et al (2018) Embedding visual hierarchy with deep networks for large-scale visual recognition. IEEE Trans Image Process 27(10):4740\u20134755","journal-title":"IEEE Trans Image Process"},{"key":"2039_CR43","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.patcog.2017.01.029","volume":"67","author":"Y Zheng","year":"2017","unstructured":"Zheng Y, Fan J, Zhang J et al (2017) Hierarchical learning of multi-task sparse metrics for large-scale image classification. Pattern Recognit 67:97\u2013109","journal-title":"Pattern Recognit"},{"key":"2039_CR44","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.1109\/TMM.2022.3154592","volume":"25","author":"P Zhu","year":"2022","unstructured":"Zhu P, Yao X, Wang Y et al (2022) Latent heterogeneous graph network for incomplete multi-view learning. IEEE Trans Multimed 25:3033\u20133045","journal-title":"IEEE Trans Multimed"},{"key":"2039_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108820","volume":"131","author":"P Zhu","year":"2022","unstructured":"Zhu P, Zhu Z, Wang Y et al (2022) Multi-granularity episodic contrastive learning for few-shot learning. Pattern Recognit 131:108820","journal-title":"Pattern Recognit"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02039-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02039-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02039-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T05:32:59Z","timestamp":1716442379000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02039-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":45,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["2039"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02039-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,22]]},"assertion":[{"value":"11 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We 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":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors agree to participate and approve the final manuscript and the submission to this journal","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate and for publication"}}]}}