{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:43:26Z","timestamp":1743147806954,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031453670"},{"type":"electronic","value":"9783031453687"}],"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\/978-3-031-45368-7_6","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T20:37:45Z","timestamp":1697056665000},"page":"78-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Community Detection for\u00a0Multi-label Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3322-6407","authenticated-orcid":false,"given":"Elaine Cec\u00edlia","family":"Gatto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9046-9499","authenticated-orcid":false,"given":"Alan Dem\u00e9trius Baria","family":"Valejo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4248-1207","authenticated-orcid":false,"given":"Mauri","family":"Ferrandin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2582-1695","authenticated-orcid":false,"given":"Ricardo","family":"Cerri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Basgalupp, M., Cerri, R., Schietgat, L., Triguero, I., Vens, C.: Beyond global and local multi-target learning. Inf. Sci. 579, 508\u2013524 (2021)","DOI":"10.1016\/j.ins.2021.08.022"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008)","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Bogatinovski, J., Todorovski, L., D\u017eeroski, S., Kocev, D.: Comprehensive comparative study of multi-label classification methods. Expert Syst. Appl. 203, 117215 (2022)","DOI":"10.1016\/j.eswa.2022.117215"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Chang, W., Yu, H., Zhong, K., Yang, Y., Dhillon, I.S.: A modular deep learning approach for extreme multi-label text classification. CoRR abs\/1905.02331 (2019)","DOI":"10.1145\/3394486.3403368"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Charte, F., Rivera, A., del Jesus, M.J., Herrera, F.: On the impact of dataset complexity and sampling strategy in multilabel classifiers performance. In: Hybrid Artificial Intelligent Systems, pp. 500\u2013511. Springer International Publishing (2016)","DOI":"10.1007\/978-3-319-32034-2_42"},{"key":"6_CR6","unstructured":"Choi, S., Cha, S., Tappert, C.C.: A survey of binary similarity and distance measures. J. Systemics Cybern. Inform. 8, 43\u201348 (2010)"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Chu, Y., et al.: DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Briefings in Bioinform. 22, bbaa205 (2020)","DOI":"10.1093\/bib\/bbaa205"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Clare, A., King, R.D.: Predicting gene function in saccharomyces cerevisiae. In: Proceedings of the European Conference on Computational Biology (ECCB 2003), September 27\u201330, 2003, Paris, France, pp. 42\u201349 (2003)","DOI":"10.1093\/bioinformatics\/btg1058"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)","DOI":"10.1103\/PhysRevE.70.066111"},{"key":"6_CR10","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Garg, A., Enright, C.G., Madden, M.G.: On asymmetric similarity search. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (2015)","DOI":"10.1109\/ICMLA.2015.128"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Gatto, E.C., Ferrandin, M., Cerri, R.: Exploring label correlations for partitioning the label space in multi-label classification. In: 2021 International Joint Conference on Neural Networks (IJCNN) (2021)","DOI":"10.1109\/IJCNN52387.2021.9533331"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Lin, S.C., Chen, C.J., Lee, T.J.: A multi-label classification with hybrid label-based meta-learning method in internet of things. IEEE Access 8, 42261\u201342269 (2020)","DOI":"10.1109\/ACCESS.2020.2976851"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Luaces, O., D\u00edez, J., Barranquero, et al., J.: Binary relevance efficacy for multilabel classification. Progress in Artificial Intelligence (2012)","DOI":"10.1007\/s13748-012-0030-x"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Melo, A., Paulheim, H.: Local and global feature selection for multilabel classification with binary relevance an empirical comparison on flat and hierarchical problems (2017)","DOI":"10.1007\/s10462-017-9556-4"},{"key":"6_CR16","unstructured":"Mezo, I.: The r-bell numbers. J. Integer Sequences 14, A11 (2011)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Mittal, R., Bhatia, M.P.S.: Classification and comparative evaluation of community detection algorithms. Archives of Computational Methods in Engineering (2020)","DOI":"10.1007\/s11831-020-09421-5"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)","DOI":"10.1103\/PhysRevE.69.026113"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T., Nguyen, T.T.T., Luong, A.V., Nguyen, Q.V.H., Liew, A.W.C., Stantic, B.: Multi-label classification via label correlation and first order feature dependance in a data stream. Pattern Recogn. 90, 35\u201351 (2019)","DOI":"10.1016\/j.patcog.2019.01.007"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Pliakos, K., Vens, C., Tsoumakas, G.: Predicting drug-target interactions with multi-label classification and label partitioning. IEEE\/ACM Trans. Comput. Biol. Bioinform. 18 1596\u20131607 (2021)","DOI":"10.1109\/TCBB.2019.2951378"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Pons, P., Latapy, M.: Computing communities in large networks using random walks (long version) (2005)","DOI":"10.1007\/11569596_31"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)","DOI":"10.1103\/PhysRevE.76.036106"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains: a review and perspectives. J. Artif. Intell. Res. 70, 683\u2013718 (2021)","DOI":"10.1613\/jair.1.12376"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006)","DOI":"10.1103\/PhysRevE.74.016110"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Rivolli, A., Read, J., Soares, C., Pfahringer, B., de Leon Ferreira de Carvalho, A.C.P.: An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning (2020)","DOI":"10.1007\/s10994-020-05879-3"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Rivolli, A., Soares, C., Carvalho, A.C.P.d.L.F.d.: Enhancing multilabel classification for food truck recommendation. Expert Systems (2018)","DOI":"10.1111\/exsy.12304"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences 105 (2008)","DOI":"10.1073\/pnas.0706851105"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"6_CR29","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-642-23808-6_10","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Sechidis","year":"2011","unstructured":"Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 145\u2013158. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23808-6_10"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (2020)","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"6_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-17290-3","volume-title":"Machine Learning in Complex Networks","author":"TC Silva","year":"2016","unstructured":"Silva, T.C., Zhao, L.: Machine Learning in Complex Networks. Springer Publishing Company, Incorporated (2016)"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Szyma\u0144ski, P., Kajdanowicz, T., Kersting, K.: How is a data-driven approach better than random choice in label space division for multi-label classification? Entropy 18 (2016)","DOI":"10.3390\/e18080282"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Tahir, M.A.U.H., Asghar, S., Manzoor, A., Noor, M.A.: A classification model for class imbalance dataset using genetic programming. IEEE Access 7, 71013\u201371037 (2019)","DOI":"10.1109\/ACCESS.2019.2915611"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Vens, C., Struyf, J., Schietgat, L., D\u017eeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Mach. Learn. 73, 185\u2013214 (2008)","DOI":"10.1007\/s10994-008-5077-3"},{"key":"6_CR35","unstructured":"Warrens, M.J.: Similarity coefficients for binary data: Properties of coefficients, coefficient matrices, multi-way metrics and multivariate coefficients. Master\u2019s thesis, Leiden University (2008)"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, M.L., Li, Y.K., Liu, X.Y., Geng, X.: Binary relevance for multi-label learning: an overview. Front. Comput. Sci. 12, 191\u2013202 (2018)","DOI":"10.1007\/s11704-017-7031-7"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, M.L., Zhou, Z.H.: Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18, 1338\u20131351 (2006)","DOI":"10.1109\/TKDE.2006.162"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, M.L., Zhou, Z.H.: Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn. 40, 2038\u20132048 (2007)","DOI":"10.1016\/j.patcog.2006.12.019"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, J.P., Chen, L., Guo, Z.H., Hancock, J.: Iatc-nrakel: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs. Bioinformatics 36, 1391\u20131396 (2020)","DOI":"10.1093\/bioinformatics\/btz757"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45368-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:27:55Z","timestamp":1709828875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45368-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453670","9783031453687"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45368-7_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":"12 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belo Horizonte","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bracis.dcc.ufmg.br","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"242","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":"90","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":"37% - 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":"4","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}