{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:58:06Z","timestamp":1742997486749,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030003739"},{"type":"electronic","value":"9783030003746"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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\/978-3-030-00374-6_12","type":"book-chapter","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T03:43:45Z","timestamp":1537933425000},"page":"118-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adapting Hierarchical Multiclass Classification to Changes in the Target Concept"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7240-590X","authenticated-orcid":false,"given":"Daniel","family":"Silva-Palacios","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8975-1120","authenticated-orcid":false,"given":"Cesar","family":"Ferri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0559-3568","authenticated-orcid":false,"given":"M. Jose","family":"Ramirez-Quintana","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"key":"12_CR1","unstructured":"Alcal\u00e1-Fdez, J., et al.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Log. Soft Comput. 17(2\u20133), 255\u2013287 (2011)"},{"key":"12_CR2","unstructured":"Cauwenberghs, G., Poggio, T.: Incremental and decremental SVM learning. In: Advances in Neural Information Processing Systems, pp. 409\u2013415 (2001)"},{"issue":"1","key":"12_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40537-016-0040-9","volume":"3","author":"MB Chandak","year":"2016","unstructured":"Chandak, M.B.: Role of big-data in classification and novel class detection in data streams. J. Big Data 3(1), 5 (2016)","journal-title":"J. Big Data"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Da, Q., Yu, Y., Zhou, Z.H.: Learning with augmented class by exploiting unlabeled data. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)","DOI":"10.1609\/aaai.v28i1.8997"},{"issue":"15","key":"12_CR5","doi-asserted-by":"publisher","first-page":"5895","DOI":"10.1016\/j.eswa.2013.05.001","volume":"40","author":"DM Farid","year":"2013","unstructured":"Farid, D.M., et al.: An adaptive ensemble classifier for mining concept drifting data streams. Expert. Syst. Appl. 40(15), 5895\u20135906 (2013)","journal-title":"Expert. Syst. Appl."},{"key":"12_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/3-540-44716-4_15","volume-title":"Functional and Logic Programming","author":"C Ferri-Ram\u00edrez","year":"2001","unstructured":"Ferri-Ram\u00edrez, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233\u2013247. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44716-4_15"},{"key":"12_CR7","first-page":"2677","volume":"9","author":"S Garc\u00eda","year":"2008","unstructured":"Garc\u00eda, S., Herrera, F.: An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677\u20132694 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"12_CR8","first-page":"215","volume":"13","author":"C Giraud-Carrier","year":"2000","unstructured":"Giraud-Carrier, C.: A note on the utility of incremental learning. AI Commun. 13(4), 215\u2013223 (2000)","journal-title":"AI Commun."},{"issue":"5","key":"12_CR9","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3233\/AIC-160705","volume":"29","author":"J Hern\u00e1ndez-Orallo","year":"2016","unstructured":"Hern\u00e1ndez-Orallo, J., et al.: Reframing in context: a systematic approach for model reuse in machine learning. AI Commun. 29(5), 551\u2013566 (2016)","journal-title":"AI Commun."},{"key":"12_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/978-3-319-10578-9_26","volume-title":"Computer Vision \u2013 ECCV 2014","author":"LP Jain","year":"2014","unstructured":"Jain, L.P., Scheirer, W.J., Boult, T.E.: Multi-class open set recognition using probability of inclusion. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 393\u2013409. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_26"},{"key":"12_CR11","unstructured":"Klinkenberg, R., Joachims, T.: Detecting concept drift with support vector machines. In: ICML, pp. 487\u2013494 (2000)"},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","volume":"45","author":"JG Moreno-Torres","year":"2012","unstructured":"Moreno-Torres, J.G., Raeder, T., Alaiz-Rodr\u00edGuez, R.: A unifying view on dataset shift in classification. Pattern Recognit. 45, 521\u2013530 (2012)","journal-title":"Pattern Recognit."},{"issue":"8","key":"12_CR13","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1109\/TKDE.2017.2691702","volume":"29","author":"X Mu","year":"2017","unstructured":"Mu, X., Ting, K.M., Zhou, Z.H.: Classification under streaming emerging new classes: a solution using completely-random trees. IEEE Trans. Knowl. Data Eng. 29(8), 1605\u20131618 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"12_CR14","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1109\/TNN.2008.2008326","volume":"20","author":"MD Muhlbaier","year":"2009","unstructured":"Muhlbaier, M.D., Topalis, A., Polikar, R.: Learn $$^{++}$$.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes. IEEE Trans. Neural Netw. 20(1), 152\u2013168 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"key":"12_CR15","volume-title":"Dataset Shift in Machine Learning","author":"J Quionero-Candela","year":"2009","unstructured":"Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. The MIT Press, Cambridge (2009)"},{"issue":"1\u20133","key":"12_CR16","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"DA Ross","year":"2008","unstructured":"Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1\u20133), 125\u2013141 (2008)","journal-title":"Int. J. Comput. Vis."},{"issue":"11","key":"12_CR17","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1109\/TPAMI.2014.2321392","volume":"36","author":"WJ Scheirer","year":"2014","unstructured":"Scheirer, W.J., Jain, L.P.: Probability models for open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2317\u20132324 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"12_CR18","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2013","unstructured":"Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1757\u20131772 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"12_CR19","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.jocs.2018.01.006","volume":"26","author":"D Silva-Palacios","year":"2018","unstructured":"Silva-Palacios, D., Ferri, C., Ram\u00edrez-Quintana, M.J.: Probabilistic class hierarchies for multiclass classification. J. Comput. Sci. 26, 254\u2013263 (2018)","journal-title":"J. Comput. Sci."},{"key":"12_CR20","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.neucom.2015.01.037","volume":"158","author":"P ZareMoodi","year":"2015","unstructured":"ZareMoodi, P., Beigy, H., Siahroudi, S.K.: Novel class detection in data streams using local patterns and neighborhood graph. Neurocomputing 158, 234\u2013245 (2015)","journal-title":"Neurocomputing"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, B.F., Su, J.S., Xu, X.: A class-incremental learning method for multi-class support vector machines in text classification, pp. 2581\u20132585. IEEE (2006)","DOI":"10.1109\/ICMLC.2006.258853"}],"container-title":["Lecture Notes in Computer Science","Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00374-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:45:50Z","timestamp":1710261950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00374-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030003739","9783030003746"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00374-6_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"27 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAEPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference of the Spanish Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"23 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caepia2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sci2s.ugr.es\/caepia18\/","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":"240","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":"36","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":"15% - 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":"3,1","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":"2,1","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)"}}]}}