{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:21:52Z","timestamp":1743034912872,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031427947"},{"type":"electronic","value":"9783031427954"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42795-4_6","type":"book-chapter","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T12:03:07Z","timestamp":1692792187000},"page":"59-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GNN-DES: A New End-to-End Dynamic Ensemble Selection Method Based on Multi-label Graph Neural Network"],"prefix":"10.1007","author":[{"given":"Mariana","family":"de Araujo Souza","sequence":"first","affiliation":[]},{"given":"Robert","family":"Sabourin","sequence":"additional","affiliation":[]},{"given":"George Darmiton","family":"da Cunha Cavalcanti","sequence":"additional","affiliation":[]},{"given":"Rafael Menelau Oliveira","family":"e Cruz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.patcog.2017.11.017","volume":"76","author":"G Armano","year":"2018","unstructured":"Armano, G., Tamponi, E.: Building forests of local trees. Pattern Recogn. 76, 380\u2013390 (2018)","journal-title":"Pattern Recogn."},{"issue":"9","key":"6_CR2","doi-asserted-by":"publisher","first-page":"3544","DOI":"10.1016\/j.patcog.2012.02.034","volume":"45","author":"PR Cavalin","year":"2012","unstructured":"Cavalin, P.R., Sabourin, R., Suen, C.Y.: LoGID: an adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs. Pattern Recogn. 45(9), 3544\u20133556 (2012)","journal-title":"Pattern Recogn."},{"issue":"5","key":"6_CR3","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1016\/j.patcog.2014.12.003","volume":"48","author":"RMO Cruz","year":"2015","unstructured":"Cruz, R.M.O., Sabourin, R., Cavalcanti, G.D.C., Ren, T.I.: META-DES: a dynamic ensemble selection framework using meta-learning. Pattern Recogn. 48(5), 1925\u20131935 (2015)","journal-title":"Pattern Recogn."},{"issue":"8","key":"6_CR4","first-page":"1","volume":"21","author":"RMO Cruz","year":"2020","unstructured":"Cruz, R.M.O., Hafemann, L.G., Sabourin, R., Cavalcanti, G.D.C.: DESlib: a dynamic ensemble selection library in python. J. Mach. Learn. Res. 21(8), 1\u20135 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.inffus.2017.09.010","volume":"41","author":"RM Cruz","year":"2018","unstructured":"Cruz, R.M., Sabourin, R., Cavalcanti, G.D.: Dynamic classifier selection: recent advances and perspectives. Inf. Fusion 41, 195\u2013216 (2018)","journal-title":"Inf. Fusion"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Davtalab, R., Cruz, R.M., Sabourin, R.: Dynamic ensemble selection using fuzzy hyperboxes. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139 (2022)","DOI":"10.1109\/IJCNN55064.2022.9892635"},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.future.2020.10.005","volume":"115","author":"S El-Sappagh","year":"2021","unstructured":"El-Sappagh, S., et al.: Alzheimer\u2019s disease progression detection model based on an early fusion of cost-effective multimodal data. Futur. Gener. Comput. Syst. 115, 680\u2013699 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119\u2013139 (1997)","journal-title":"J. Comput. Syst. Sci."},{"key":"6_CR9","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"issue":"3","key":"6_CR10","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/34.990132","volume":"24","author":"TK Ho","year":"2002","unstructured":"Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289\u2013300 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Ko, A.H.-R., Sabourin, R., de Souza Britto Jr., A.: A new dynamic ensemble selection method for numeral recognition. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 431\u2013439. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-72523-7_43","DOI":"10.1007\/978-3-540-72523-7_43"},{"issue":"2","key":"6_CR13","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/34.982906","volume":"24","author":"LI Kuncheva","year":"2002","unstructured":"Kuncheva, L.I.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 281\u2013286 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"6_CR14","doi-asserted-by":"publisher","first-page":"3188","DOI":"10.1007\/s10489-019-01435-2","volume":"49","author":"D Li","year":"2019","unstructured":"Li, D., Wen, G., Li, X., Cai, X.: Graph-based dynamic ensemble pruning for facial expression recognition. Appl. Intell. 49(9), 3188\u20133206 (2019)","journal-title":"Appl. Intell."},{"issue":"1","key":"6_CR15","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2011.03.054","volume":"75","author":"AC Lorena","year":"2012","unstructured":"Lorena, A.C., Costa, I.G., Spola\u00f4r, N., De Souto, M.C.: Analysis of complexity indices for classification problems: cancer gene expression data. Neurocomputing 75(1), 33\u201342 (2012)","journal-title":"Neurocomputing"},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-71249-9_11","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Narassiguin","year":"2017","unstructured":"Narassiguin, A., Elghazel, H., Aussem, A.: Dynamic ensemble selection with probabilistic classifier chains. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 169\u2013186. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71249-9_11"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Oliveira, D.V., Cavalcanti, G.D., Porpino, T.N., Cruz, R.M., Sabourin, R.: K-nearest oracles borderline dynamic classifier ensemble selection. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489737"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Pereira, M., Britto, A., Oliveira, L., Sabourin, R.: Dynamic ensemble selection by K-nearest local Oracles with discrimination index. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence, pp. 765\u2013771. IEEE (2018)","DOI":"10.1109\/ICTAI.2018.00120"},{"key":"6_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-319-46128-1_26","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"F Pinto","year":"2016","unstructured":"Pinto, F., Soares, C., Mendes-Moreira, J.: CHADE: metalearning with classifier chains for dynamic combination of classifiers. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 410\u2013425. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46128-1_26"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Salehi, A., Davulcu, H.: Graph attention auto-encoders. In: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence, pp. 989\u2013996 (2020)","DOI":"10.1109\/ICTAI50040.2020.00154"},{"issue":"3","key":"6_CR21","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10044-007-0061-2","volume":"10","author":"JS S\u00e1nchez","year":"2007","unstructured":"S\u00e1nchez, J.S., Mollineda, R.A., Sotoca, J.M.: An analysis of how training data complexity affects the nearest neighbor classifiers. Pattern Anal. Appl. 10(3), 189\u2013201 (2007)","journal-title":"Pattern Anal. Appl."},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Soares, R.G., Santana, A., Canuto, A.M., de Souto, M.C.P.: Using accuracy and diversity to select classifiers to build ensembles. In: The 2006 IEEE International Joint Conference on Neural Network (IJCNN) Proceedings, pp. 1310\u20131316 (2006)","DOI":"10.1109\/IJCNN.2006.246844"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.patcog.2018.08.004","volume":"85","author":"MA Souza","year":"2019","unstructured":"Souza, M.A., Cavalcanti, G.D., Cruz, R.M., Sabourin, R.: Online local pool generation for dynamic classifier selection. Pattern Recogn. 85, 132\u2013148 (2019)","journal-title":"Pattern Recogn."},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Souza, M.A., Sabourin, R., Cavalcanti, G.D.C., Cruz, R.M.O.: Local overlap reduction procedure for dynamic ensemble selection. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139 (2022)","DOI":"10.1109\/IJCNN55064.2022.9892846"},{"key":"6_CR25","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.inffus.2022.09.010","volume":"90","author":"MA Souza","year":"2023","unstructured":"Souza, M.A., Sabourin, R., Cavalcanti, G.D., Cruz, R.M.: OLP++: an online local classifier for high dimensional data. Inf. Fusion 90, 120\u2013137 (2023)","journal-title":"Inf. Fusion"},{"key":"6_CR26","unstructured":"Vandaele, R., Kang, B., De Bie, T., Saeys, Y.: The curse revisited: when are distances informative for the ground truth in noisy high-dimensional data? In: International Conference on Artificial Intelligence and Statistics, pp. 2158\u20132172. PMLR (2022)"},{"issue":"2","key":"6_CR27","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2013","unstructured":"Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. SIGKDD Explor. 15(2), 49\u201360 (2013)","journal-title":"SIGKDD Explor."},{"key":"6_CR28","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"issue":"10","key":"6_CR29","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.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn. 44(10), 2656\u20132668 (2011)","journal-title":"Pattern Recogn."},{"issue":"2","key":"6_CR30","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","volume":"2","author":"F Xia","year":"2021","unstructured":"Xia, F., et al.: Graph learning: a survey. IEEE Trans. Artif. Intell. 2(2), 109\u2013127 (2021)","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"10","key":"6_CR31","doi-asserted-by":"publisher","first-page":"4663","DOI":"10.1109\/TKDE.2021.3049250","volume":"34","author":"S Zhang","year":"2022","unstructured":"Zhang, S.: Challenges in KNN classification. IEEE Trans. Knowl. Data Eng. 34(10), 4663\u20134675 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Graph-Based Representations in Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42795-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:28:54Z","timestamp":1710246534000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42795-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031427947","9783031427954"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42795-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"24 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GbRPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Graph-Based Representations in Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietri sul Mare","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gbrpr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/gbr2023.unisa.it","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"89% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}