{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:52:36Z","timestamp":1740099156343,"version":"3.37.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030008390"},{"type":"electronic","value":"9783030008406"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-00840-6_14","type":"book-chapter","created":{"date-parts":[[2018,9,16]],"date-time":"2018-09-16T06:35:33Z","timestamp":1537079733000},"page":"121-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Representation and Semi-clustering Approach for Label Space Reduction in Multi-label Classification of\u00a0Documents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2572-9450","authenticated-orcid":false,"given":"Rafa\u0142","family":"Wo\u017aniak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1546-4823","authenticated-orcid":false,"given":"Danuta","family":"Zakrzewska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,16]]},"reference":[{"key":"14_CR1","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of the ECML\/PKDD Workshop on Mining Multidimensional Data, Antwerp, Belgium, pp. 30\u201344 (2008)"},{"key":"14_CR2","unstructured":"Balasubramanian, K., Lebanon, G.: The landmark selection method for multiple output prediction. In: Proceedings of the 29th International Conference on Machine Learning, pp. 283\u2013290. Omni Press, Edinburgh (2012)"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: Proceedings of the 2008 8th IEEE International Conference on Data Mining, pp. 995\u20131000. IEEE Computer Society, Washington, DC (2008)","DOI":"10.1109\/ICDM.2008.74"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Bi, W., Kwok, J.: Efficient multi-label classification with many labels. In: Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA, vol. 28, pp. 405\u2013413 (2013)","DOI":"10.1609\/aaai.v28i1.8996"},{"key":"14_CR5","first-page":"772","volume-title":"Advances in Neural Information Processing Systems","author":"D Hsu","year":"2009","unstructured":"Hsu, D., Kakade, S.M., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 772\u2013780. Curran Associates Inc., Vancouver (2009)"},{"key":"14_CR6","series-title":"Problem Analysis, Metrics and Techniques","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41111-8","volume-title":"Multilabel Classification","author":"F Herrera","year":"2016","unstructured":"Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J.: Multilabel Classification. Problem Analysis, Metrics and Techniques. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41111-8"},{"issue":"8","key":"14_CR7","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M Zhang","year":"2014","unstructured":"Zhang, M., Zhou, Z.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819\u20131837 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"14_CR8","first-page":"213","volume":"7","author":"R Wo\u017aniak","year":"2018","unstructured":"Wo\u017aniak, R., O\u017cd\u017cy\u0144ski, P., Zakrzewska, D.: Cluster analysis of medical text documents by using semi-clustering approach based on graph representation. Inf. Syst. Manag. 7(3), 213\u2013224 (2018)","journal-title":"Inf. Syst. Manag."},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Glinka, K., Wo\u017aniak, R., Zakrzewska, D.: Improving multi-label medical text classification by feature selection. In: Proceedings of the 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 176\u2013181. IEEE Computer Society, Pozna\u0144 (2017)","DOI":"10.1109\/WETICE.2017.42"},{"key":"14_CR10","volume-title":"Natural Language Processing with Python","author":"S Bird","year":"2009","unstructured":"Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O\u2019Reilly Media Inc., Sebastopol (2009)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Andersen, J.S., Zukunft, O.: Semi-clustering that scales: an empirical evaluation of GraphX. In: Proceedings of the 2016 IEEE International Congress on Big Data, pp. 333\u2013336. IEEE Computer Society, San Francisco (2016)","DOI":"10.1109\/BigDataCongress.2016.51"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Malewicz, G., et al.: Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 International Conference on Management of Data, pp. 135\u2013146. ACM, Indianapolis (2010)","DOI":"10.1145\/1807167.1807184"},{"key":"14_CR13","unstructured":"Ohsumed: text categorization corpus. http:\/\/disi.unitn.it\/moschitti\/corpora.htm . Accessed 6 June 2018"},{"key":"14_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/BFb0026683","volume-title":"Machine Learning: ECML-98","author":"T Joachims","year":"1998","unstructured":"Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: N\u00e9dellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137\u2013142. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0026683"},{"key":"14_CR15","unstructured":"Weka 3: data mining software in Java. https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/ . Accessed 6 June 2018"},{"key":"14_CR16","unstructured":"Mulan: a Java library for multi-label learning. http:\/\/mulan.sourceforge.net\/ . Accessed 6 June 2018"},{"key":"14_CR17","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":"14_CR18","unstructured":"Okapi: most advanced open-source machine learning library for Apache Giraph. http:\/\/grafos.ml\/okapi.html . Accessed 6 June 2018"},{"key":"14_CR19","unstructured":"NetworkX: Python software for complex networks. https:\/\/networkx.github.io\/ . Accessed 6 June 2018"}],"container-title":["Communications in Computer and Information Science","Computer and Information Sciences"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00840-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T14:03:23Z","timestamp":1662041003000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-00840-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030008390","9783030008406"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00840-6_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"ISCIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Computer and Information Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poznan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iscis2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/projekty.iitis.pl\/iscis2018","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}