{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:34:09Z","timestamp":1769092449015,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,7,30]],"date-time":"2016-07-30T00:00:00Z","timestamp":1469836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union within European Social Fund; The European Commission under the 7th Framework Programme","award":["316097"],"award-info":[{"award-number":["316097"]}]},{"DOI":"10.13039\/501100004442","name":"National Science Centre","doi-asserted-by":"publisher","award":["DEC-2013\/09\/B\/ST6\/02317"],"award-info":[{"award-number":["DEC-2013\/09\/B\/ST6\/02317"]}],"id":[{"id":"10.13039\/501100004442","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union within the Horizon 2020 Marie Sk\u0142odowska-Curie research and innovation program","award":["691152"],"award-info":[{"award-number":["691152"]}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Collaborative Research Center SFB 876 Project A6"],"award-info":[{"award-number":["Collaborative Research Center SFB 876 Project A6"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wroc\u0142aw University of Science and Technology","award":["Statutory Funds"],"award-info":[{"award-number":["Statutory Funds"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We propose using five data-driven community detection approaches from social networks to partition the label space in the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd. We evaluate modularity-maximizing using fast greedy and leading eigenvector approximations, infomap, walktrap and label propagation algorithms. For this purpose, we propose to construct a label co-occurrence graph (both weighted and unweighted versions) based on training data and perform community detection to partition the label set. Then, each partition constitutes a label space for separate multi-label classification sub-problems. As a result, we obtain an ensemble of multi-label classifiers that jointly covers the whole label space. Based on the binary relevance and label powerset classification methods, we compare community detection methods to label space divisions against random baselines on 12 benchmark datasets over five evaluation measures. We discover that data-driven approaches are more efficient and more likely to outperform RAkELd than binary relevance or label powerset is, in every evaluated measure. For all measures, apart from Hamming loss, data-driven approaches are significantly better than RAkELd (    \u03b1 = 0 . 05    ), and at least one data-driven approach is more likely to outperform RAkELd than a priori methods in the case of RAkELd\u2019s best performance. This is the largest RAkELd evaluation published to date with 250 samplings per value for 10 values of RAkELd parameter k on 12 datasets published to date.<\/jats:p>","DOI":"10.3390\/e18080282","type":"journal-article","created":{"date-parts":[[2016,8,3]],"date-time":"2016-08-03T03:47:39Z","timestamp":1470196059000},"page":"282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7733-3239","authenticated-orcid":false,"given":"Piotr","family":"Szyma\u0144ski","sequence":"first","affiliation":[{"name":"Department of Computational Intelligence, Wroc\u0142aw University of Technology, Wybrze\u017ce Stanis\u0142awa Wyspia\u0144skiego 27, 50-370 Wroc\u0142aw, Poland"},{"name":"Illimites Foundation, Gajowicka 64 lok. 1, 53-422 Wroc\u0142aw, Poland"}]},{"given":"Tomasz","family":"Kajdanowicz","sequence":"additional","affiliation":[{"name":"Department of Computational Intelligence, Wroc\u0142aw University of Technology, Wybrze\u017ce Stanis\u0142awa Wyspia\u0144skiego 27, 50-370 Wroc\u0142aw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2873-9152","authenticated-orcid":false,"given":"Kristian","family":"Kersting","sequence":"additional","affiliation":[{"name":"Department of Computer Science, TU Dortmund University, August-Schmidt-Stra\u00dfe 4, 44221 Dortmund, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/jdwm.2007070101","article-title":"Multi-label classification: An overview","volume":"3","author":"Tsoumakas","year":"2007","journal-title":"Int. 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