{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:34:14Z","timestamp":1742913254154,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030873332"},{"type":"electronic","value":"9783030873349"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87334-9_14","type":"book-chapter","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T14:50:32Z","timestamp":1631890232000},"page":"164-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets"],"prefix":"10.1007","author":[{"given":"Andrea","family":"Campagner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8083-7809","authenticated-orcid":false,"given":"Davide","family":"Ciucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Arora, S., Barak, B.: Computational Complexity: A modern Approach. Cambridge University Press, Cambridge (2009)","DOI":"10.1017\/CBO9780511804090"},{"key":"14_CR2","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-54966-8_5","volume-title":"Thriving Rough Sets","author":"R Bello","year":"2017","unstructured":"Bello, R., Falcon, R.: Rough sets in machine learning: a review. In: Wang, G., Skowron, A., Yao, Y., \u015al\u0119zak, D., Polkowski, L. (eds.) Thriving Rough Sets. SCI, vol. 708, pp. 87\u2013118. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54966-8_5"},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.knosys.2019.05.018","volume":"180","author":"A Campagner","year":"2019","unstructured":"Campagner, A., Ciucci, D.: Orthopartitions and soft clustering: soft mutual information measures for clustering validation. Knowl.-Based Syst. 180, 51\u201361 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"14_CR4","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-030-50146-4_35","volume-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems","author":"A Campagner","year":"2020","unstructured":"Campagner, A., Ciucci, D., H\u00fcllermeier, E.: Feature reduction in superset learning using rough sets and evidence theory. In: Lesot, M.J., et al. (eds.) IPMU 2020. CCIS, vol. 1237, pp. 471\u2013484. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50146-4_35"},{"key":"14_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-319-60840-2_3","volume-title":"Rough Sets","author":"D Ciucci","year":"2017","unstructured":"Ciucci, D., Forcati, I.: Certainty-based rough sets. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 43\u201355. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60840-2_3"},{"issue":"3","key":"14_CR6","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.patcog.2008.07.014","volume":"42","author":"E C\u00f4me","year":"2009","unstructured":"C\u00f4me, E., Oukhellou, L., Denoeux, T., Aknin, P.: Learning from partially supervised data using mixture models and belief functions. Pattern Recogn. 42(3), 334\u2013348 (2009)","journal-title":"Pattern Recogn."},{"issue":"1","key":"14_CR7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MCI.2018.2881642","volume":"14","author":"I Couso","year":"2019","unstructured":"Couso, I., Borgelt, C., Hullermeier, E., Kruse, R.: Fuzzy sets in data analysis: from statistical foundations to machine learning. IEEE Comput. Intell. Mag. 14(1), 31\u201344 (2019)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Couso, I., Dubois, D., S\u00e1nchez, L.: Random sets and random fuzzy sets as ill-perceived random variables. SpringerBriefs in Computational Intelligence (2014)","DOI":"10.1007\/978-3-319-08611-8"},{"key":"14_CR9","doi-asserted-by":"publisher","unstructured":"Denoeux, T.: A k-nearest neighbor classification rule based on dempster-shafer theory. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol. 219, pp. 737\u2013760. Springer, Berlin, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-44792-4_29","DOI":"10.1007\/978-3-540-44792-4_29"},{"issue":"3","key":"14_CR10","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/S0165-0114(00)00086-5","volume":"122","author":"T Den\u0153ux","year":"2001","unstructured":"Den\u0153ux, T., Zouhal, L.M.: Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets Syst. 122(3), 409\u2013424 (2001)","journal-title":"Fuzzy Sets Syst."},{"key":"14_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/11829898_7","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"N El Gayar","year":"2006","unstructured":"El Gayar, N., Schwenker, F., Palm, G.: A study of the robustness of KNN classifiers trained using soft labels. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol. 4087, pp. 67\u201380. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11829898_7"},{"issue":"1","key":"14_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0377-2217(00)00167-3","volume":"129","author":"S Greco","year":"2001","unstructured":"Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1), 1\u201347 (2001)","journal-title":"Eur. J. Oper. Res."},{"issue":"7","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/j.ijar.2013.09.003","volume":"55","author":"E H\u00fcllermeier","year":"2014","unstructured":"H\u00fcllermeier, E.: Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization. Int. J. Approx. Reason. 55(7), 1519\u20131534 (2014)","journal-title":"Int. J. Approx. Reason."},{"key":"14_CR14","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.fss.2015.09.001","volume":"281","author":"E H\u00fcllermeier","year":"2015","unstructured":"H\u00fcllermeier, E.: Does machine learning need fuzzy logic? Fuzzy Sets Syst. 281, 292\u2013299 (2015)","journal-title":"Fuzzy Sets Syst."},{"issue":"5","key":"14_CR15","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3233\/IDA-2006-10503","volume":"10","author":"E H\u00fcllermeier","year":"2006","unstructured":"H\u00fcllermeier, E., Beringer, J.: Learning from ambiguously labeled examples. Intell. Data Anal. 10(5), 419\u2013439 (2006)","journal-title":"Intell. Data Anal."},{"key":"14_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-319-23525-7_16","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"E H\u00fcllermeier","year":"2015","unstructured":"H\u00fcllermeier, E., Cheng, W.: Superset learning based on generalized loss minimization. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9285, pp. 260\u2013275. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23525-7_16"},{"key":"14_CR17","unstructured":"Liu, L., Dietterich, T.: Learnability of the superset label learning problem. In: ICML, pp. 1629\u20131637 (2014)"},{"key":"14_CR18","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-319-08852-5_7","volume-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems","author":"M Nakata","year":"2014","unstructured":"Nakata, M., Sakai, H.: An approach based on rough sets to possibilistic information. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014. CCIS, vol. 444, pp. 61\u201370. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08852-5_7"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T., Walker, C., Walker, E.A.: A First Course in Fuzzy Logic. CRC Press, Boca Raton (2018)","DOI":"10.1201\/9780429505546"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Ning, Q., He, H., Fan, C., Roth, D.: Partial or complete, that\u2019s the question. arXiv preprint arXiv:1906.04937 (2019)","DOI":"10.18653\/v1\/N19-1227"},{"key":"14_CR21","unstructured":"Orlowska, E. (ed.): IncompleteIinformation: Rough Set Analysis. Physica (2013)"},{"issue":"5","key":"14_CR22","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341\u2013356 (1982)","journal-title":"Int. J. Comput. Inf. Sci."},{"key":"14_CR23","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.fss.2015.04.012","volume":"286","author":"B Quost","year":"2016","unstructured":"Quost, B., Denoeux, T.: Clustering and classification of fuzzy data using the fuzzy em algorithm. Fuzzy Sets Syst. 286, 134\u2013156 (2016)","journal-title":"Fuzzy Sets Syst."},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Sakai, H., Liu, C., Nakata, M., Tsumoto, S.: A proposal of a privacy-preserving questionnaire by non-deterministic information and its analysis. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1956\u20131965. IEEE (2016)","DOI":"10.1109\/BigData.2016.7840817"},{"key":"14_CR25","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-319-54966-8_9","volume-title":"Thriving Rough Sets","author":"H Sakai","year":"2017","unstructured":"Sakai, H., Nakata, M., Yao, Y.: Pawlak\u2019s many valued information system, non-deterministic information system, and a proposal of new topics on information incompleteness toward the actual application. In: Wang, G., Skowron, A., Yao, Y., \u015al\u0119zak, D., Polkowski, L. (eds.) Thriving Rough Sets. SCI, vol. 708, pp. 187\u2013204. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54966-8_9"},{"key":"14_CR26","unstructured":"Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)"},{"issue":"3\u20134","key":"14_CR27","first-page":"365","volume":"53","author":"D \u015al\u0119zak","year":"2002","unstructured":"\u015al\u0119zak, D.: Approximate entropy reducts. Fundam. Inform. 53(3\u20134), 365\u2013390 (2002)","journal-title":"Fundam. Inform."},{"key":"14_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-99368-3_49","volume-title":"Rough Sets","author":"D \u015al\u0119zak","year":"2018","unstructured":"\u015al\u0119zak, D., Dutta, S.: Dynamic and discernibility characteristics of different attribute reduction criteria. In: Nguyen, H.S., Ha, Q.-T., Li, T., Przyby\u0142a-Kasperek, M. (eds.) IJCRS 2018. LNCS (LNAI), vol. 11103, pp. 628\u2013643. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99368-3_49"},{"issue":"1","key":"14_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2008.05.006","volume":"9","author":"K Thangavel","year":"2009","unstructured":"Thangavel, K., Pethalakshmi, A.: Dimensionality reduction based on rough set theory: a review. Appl. Soft Comput. 9(1), 1\u201312 (2009)","journal-title":"Appl. Soft Comput."},{"key":"14_CR30","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/978-3-642-10646-0_19","volume-title":"Rough Sets, Fuzzy Sets, Data Mining and Granular Computing","author":"S Trabelsi","year":"2009","unstructured":"Trabelsi, S., Elouedi, Z., Lingras, P.: Dynamic reduct from partially uncertain data using rough sets. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., \u015al\u0119zak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 160\u2013167. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-10646-0_19"},{"key":"14_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/3-540-48523-6_65","volume-title":"Automata, Languages and Programming","author":"C Umans","year":"1999","unstructured":"Umans, C.: On the complexity and inapproximability of shortest implicant problems. In: Wiedermann, J., van Emde Boas, P., Nielsen, M. (eds.) ICALP 1999. LNCS, vol. 1644, pp. 687\u2013696. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-48523-6_65"},{"issue":"1\u20132","key":"14_CR32","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/S0020-0255(97)00076-5","volume":"104","author":"YY Yao","year":"1998","unstructured":"Yao, Y.Y., Lingras, P.J.: Interpretations of belief functions in the theory of rough sets. Inf. Sci. 104(1\u20132), 81\u2013106 (1998)","journal-title":"Inf. Sci."},{"issue":"1","key":"14_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0165-0114(78)90029-5","volume":"1","author":"LA Zadeh","year":"1978","unstructured":"Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3\u201328 (1978)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"14_CR34","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"Z-H Zhou","year":"2018","unstructured":"Zhou, Z.-H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5(1), 44\u201353 (2018)","journal-title":"Natl. Sci. Rev."}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87334-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T08:30:49Z","timestamp":1710232249000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87334-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873332","9783030873349"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87334-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"16 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCRS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rough Sets","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ifsa-eusflat2021.eu\/","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":"26","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":"13","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":"7","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":"50% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}