{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:35:10Z","timestamp":1763728510330,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"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":["62172442"],"award-info":[{"award-number":["62172442"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10044-023-01176-6","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T09:10:33Z","timestamp":1687425033000},"page":"1481-1492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Graph-based fine-grained model selection for multi-source domain"],"prefix":"10.1007","volume":"26","author":[{"given":"Zhigang","family":"Hu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1015-0438","authenticated-orcid":false,"given":"Yuhang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Meiguang","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"JianJun","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"1176_CR1","doi-asserted-by":"crossref","unstructured":"Tan Y, Li Y, Huang S-L (2021) Otce: a transferability metric for cross-domain cross-task representations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15779\u201315788","DOI":"10.1109\/CVPR46437.2021.01552"},{"key":"1176_CR2","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.ins.2015.05.010","volume":"317","author":"MA Mu\u00f1oz","year":"2015","unstructured":"Mu\u00f1oz MA, Sun Y, Kirley M, Halgamuge SK (2015) Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Inf Sci 317:224\u2013245","journal-title":"Inf Sci"},{"key":"1176_CR3","doi-asserted-by":"publisher","DOI":"10.1145\/2737924.2737999","author":"MK Emani","year":"2015","unstructured":"Emani MK, O\u2019Boyle M (2015) Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments. Assoc Comput Mach. https:\/\/doi.org\/10.1145\/2737924.2737999","journal-title":"Assoc Comput Mach"},{"issue":"2","key":"1176_CR4","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1111\/coin.12487","volume":"38","author":"J Sekar","year":"2022","unstructured":"Sekar J, Aruchamy P, Sulaima Lebbe Abdul H, Mohammed AS, Khamuruddeen S (2022) An efficient clinical support system for heart disease prediction using Tanfis classifier. Comput. Intell. 38(2):610\u2013640","journal-title":"Comput. Intell."},{"issue":"10","key":"1176_CR5","doi-asserted-by":"publisher","first-page":"2150178","DOI":"10.1142\/S0218126621501784","volume":"30","author":"S Jayachitra","year":"2021","unstructured":"Jayachitra S, Prasanth A (2021) Multi-feature analysis for automated brain stroke classification using weighted gaussian Na\u00efve Bayes classifier. J Circuits, Syst Comput 30(10):2150178","journal-title":"J Circuits, Syst Comput"},{"key":"1176_CR6","doi-asserted-by":"crossref","unstructured":"Shao W, Zhao X, Ge Y, Zhang Z, Yang L, Wang X, Shan Y, Luo P (2022) Not all models are equal: predicting model transferability in a self-challenging fisher space. In: Computer vision\u2014ECCV 2022: 17th European conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XXXIV. Springer, pp 286\u2013302","DOI":"10.1007\/978-3-031-19830-4_17"},{"key":"1176_CR7","unstructured":"Rivolli A, Garcia LP, Soares C, Vanschoren J, de Carvalho AC (2018) Characterizing classification datasets: a study of meta-features for meta-learning. arXiv:1808.10406"},{"key":"1176_CR8","doi-asserted-by":"publisher","unstructured":"Cohen-Shapira N, Rokach L, Shapira B, Katz G, Vainshtein R (2019) AutoGRD: model recommendation through graphical dataset representation. https:\/\/doi.org\/10.1145\/3357384.3357896","DOI":"10.1145\/3357384.3357896"},{"key":"1176_CR9","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1016\/j.ins.2021.08.028","volume":"577","author":"N Cohen-Shapira","year":"2021","unstructured":"Cohen-Shapira N, Rokach L (2021) Automatic selection of clustering algorithms using supervised graph embedding. Inf Sci 577:824\u2013851","journal-title":"Inf Sci"},{"key":"1176_CR10","first-page":"111","volume":"21","author":"E Alcoba\u00e7a","year":"2020","unstructured":"Alcoba\u00e7a E, Siqueira F, Rivolli A, Garcia LPF, Oliva JT, de Carvalho AC (2020) Mfe: towards reproducible meta-feature extraction. J Mach Learn Res 21:111\u201311115","journal-title":"J Mach Learn Res"},{"key":"1176_CR11","doi-asserted-by":"publisher","unstructured":"Taylor B, Marco VS, Wolff W, Elkhatib Y, Wang Z (2018) Adaptive deep learning model selection on embedded systems. https:\/\/doi.org\/10.1145\/3211332.3211336","DOI":"10.1145\/3211332.3211336"},{"issue":"1","key":"1176_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3371154","volume":"19","author":"VS Marco","year":"2020","unstructured":"Marco VS, Taylor B, Wang Z, Elkhatib Y (2020) Optimizing deep learning inference on embedded systems through adaptive model selection. ACM Trans Embed Comput Syst 19(1):1\u201328. https:\/\/doi.org\/10.1145\/3371154","journal-title":"ACM Trans Embed Comput Syst"},{"key":"1176_CR13","unstructured":"Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578"},{"key":"1176_CR14","doi-asserted-by":"crossref","unstructured":"Mazzawi H, Gonzalvo X, Kracun A, Sridhar P, Subrahmanya N, Lopez-Moreno I, Park H-J, Violette P (2019) Improving keyword spotting and language identification via neural architecture search at scale. In: Interspeech, pp 1278\u20131282","DOI":"10.21437\/Interspeech.2019-1916"},{"key":"1176_CR15","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.patcog.2017.07.019","volume":"73","author":"MJ Afridi","year":"2018","unstructured":"Afridi MJ, Ross A, Shapiro EM (2018) On automated source selection for transfer learning in convolutional neural networks. Pattern Recogn 73:65\u201375","journal-title":"Pattern Recogn"},{"key":"1176_CR16","unstructured":"Nguyen C, Hassner T, Seeger M, Archambeau C (2020) Leep: a new measure to evaluate transferability of learned representations. In: International conference on machine learning. PMLR, pp 7294\u20137305"},{"key":"1176_CR17","doi-asserted-by":"publisher","first-page":"6190","DOI":"10.1109\/ACCESS.2019.2963742","volume":"8","author":"A Meiseles","year":"2020","unstructured":"Meiseles A, Rokach L (2020) Source model selection for deep learning in the time series domain. IEEE Access 8:6190\u20136200","journal-title":"IEEE Access"},{"key":"1176_CR18","doi-asserted-by":"publisher","unstructured":"Deng W, Zheng L (2021) Are labels always necessary for classifier accuracy evaluation. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3136244","DOI":"10.1109\/TPAMI.2021.3136244"},{"key":"1176_CR19","doi-asserted-by":"publisher","unstructured":"Ma X, Zhang T, Xu C (2019) Gcan: graph convolutional adversarial network for unsupervised domain adaptation. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8258\u20138268. https:\/\/doi.org\/10.1109\/CVPR.2019.00846","DOI":"10.1109\/CVPR.2019.00846"},{"key":"1176_CR20","unstructured":"Luo Y, Wang Z, Huang Z, Baktashmotlagh M (2020) Progressive graph learning for open-set domain adaptation. In: International conference on machine learning. PMLR, pp 6468\u20136478"},{"key":"1176_CR21","doi-asserted-by":"crossref","unstructured":"Roy S, Krivosheev E, Zhong Z, Sebe N, Ricci E (2021) Curriculum graph co-teaching for multi-target domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5351\u20135360","DOI":"10.1109\/CVPR46437.2021.00531"},{"key":"1176_CR22","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"1176_CR23","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1176_CR24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (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"},{"key":"1176_CR25","doi-asserted-by":"crossref","unstructured":"Dwivedi K, Huang J, Cichy RM, Roig G (2020) Duality diagram similarity: a generic framework for initialization selection in task transfer learning. In: European conference on computer vision. Springer, pp 497\u2013513","DOI":"10.1007\/978-3-030-58574-7_30"},{"key":"1176_CR26","doi-asserted-by":"crossref","unstructured":"Dwivedi K, Roig G (2019) Representation similarity analysis for efficient task taxonomy & transfer learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12387\u201312396","DOI":"10.1109\/CVPR.2019.01267"},{"key":"1176_CR27","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge, p 02142. http:\/\/www.deeplearningbook.org"},{"key":"1176_CR28","doi-asserted-by":"crossref","unstructured":"Zhou K, Yang Y, Hospedales T, Xiang T (2020) Deep domain-adversarial image generation for domain generalisation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 13025\u201313032","DOI":"10.1609\/aaai.v34i07.7003"},{"key":"1176_CR29","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision. Springer, pp 213\u2013226","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"1176_CR30","doi-asserted-by":"publisher","first-page":"8008","DOI":"10.1109\/tip.2021.3112012","volume":"30","author":"K Zhou","year":"2021","unstructured":"Zhou K, Yang Y, Qiao Y, Xiang T (2021) Domain adaptive ensemble learning. IEEE Trans Image Process 30:8008\u20138018. https:\/\/doi.org\/10.1109\/tip.2021.3112012","journal-title":"IEEE Trans Image Process"},{"key":"1176_CR31","doi-asserted-by":"crossref","unstructured":"Peng X, Bai Q, Xia X, Huang Z, Saenko K, Wang B (2019) Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1406\u20131415","DOI":"10.1109\/ICCV.2019.00149"},{"key":"1176_CR32","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: alexnet-level accuracy with 50x fewer parameters and < 0.5 mb model size. arXiv:1602.07360"},{"key":"1176_CR33","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1176_CR34","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"04","key":"1176_CR35","doi-asserted-by":"publisher","first-page":"3438","DOI":"10.1609\/aaai.v34i04.5747","volume":"34","author":"D Chen","year":"2020","unstructured":"Chen D, Lin Y, Li W, Li P, Zhou J, Sun X (2020) Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. Proc AAAI Confer Artif Intell 34(04):3438\u20133445. https:\/\/doi.org\/10.1609\/aaai.v34i04.5747","journal-title":"Proc AAAI Confer Artif Intell"},{"key":"1176_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106438","volume":"210","author":"Z Ali","year":"2020","unstructured":"Ali Z, Qi G, Muhammad K, Ali B, Abro WA (2020) Paper recommendation based on heterogeneous network embedding. Knowl-Based Syst 210:106438","journal-title":"Knowl-Based Syst"},{"key":"1176_CR37","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s10044-016-0578-3","volume":"21","author":"M Sun","year":"2018","unstructured":"Sun M, Cho S (2018) Obtaining calibrated probability using roc binning. Pattern Anal Appl 21:307\u2013322","journal-title":"Pattern Anal Appl"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-023-01176-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-023-01176-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-023-01176-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T14:11:19Z","timestamp":1690035079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-023-01176-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,22]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1176"],"URL":"https:\/\/doi.org\/10.1007\/s10044-023-01176-6","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2023,6,22]]},"assertion":[{"value":"7 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}