{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:24:44Z","timestamp":1765268684470,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030553920"},{"type":"electronic","value":"9783030553937"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-55393-7_38","type":"book-chapter","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T23:12:32Z","timestamp":1597878752000},"page":"427-437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Evidential Deep Neural Networks for Uncertain Data Classification"],"prefix":"10.1007","author":[{"given":"Bin","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Xiaodong","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Thierry","family":"Denoeux","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.knosys.2017.04.008","volume":"127","author":"Y Chen","year":"2017","unstructured":"Chen, Y., Yue, X., Fujita, H., Fu, S.: Three-way decision support for diagnosis on focal liver lesions. Knowl.-Based Syst. 127, 85\u201399 (2017)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"38_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TIT.1970.1054406","volume":"16","author":"C Chow","year":"1970","unstructured":"Chow, C.: On optimum recognition error and reject tradeoff. IEEE Trans. Inf. Theory 16(1), 41\u201346 (1970)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"38_CR3","unstructured":"Cortes, C., DeSalvo, G., Mohri, M.: Boosting with abstention. In: Advances in Neural Information Processing Systems, pp. 1660\u20131668 (2016)"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Cruz-Roa, A., et al.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: Medical Imaging 2014: Digital Pathology, vol. 9041, p. 904103. International Society for Optics and Photonics (2014)","DOI":"10.1117\/12.2043872"},{"key":"38_CR5","doi-asserted-by":"publisher","unstructured":"Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. In: Classic Works of the Dempster-Shafer Theory of Belief Functions, pp. 57\u201372. Springer (2008). https:\/\/doi.org\/10.1007\/978-3-540-44792-4_3","DOI":"10.1007\/978-3-540-44792-4_3"},{"issue":"1","key":"38_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TKDE.2011.201","volume":"25","author":"T Denoeux","year":"2011","unstructured":"Denoeux, T.: Maximum likelihood estimation from uncertain data in the belief function framework. IEEE Trans. Knowl. Data Eng. 25(1), 119\u2013130 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"38_CR7","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.knosys.2019.03.030","volume":"176","author":"T Denoeux","year":"2019","unstructured":"Denoeux, T.: Logistic regression, neural networks and Dempster-Shafer theory: a new perspective. Knowl.-Based Syst. 176, 54\u201367 (2019)","journal-title":"Knowl.-Based Syst."},{"issue":"May","key":"38_CR8","first-page":"1605","volume":"11","author":"R El-Yaniv","year":"2010","unstructured":"El-Yaniv, R., Wiener, Y.: On the foundations of noise-free selective classification. J. Mach. Learn. Res. 11(May), 1605\u20131641 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"38_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/3-540-45665-1_6","volume-title":"Pattern Recognition with Support Vector Machines","author":"G Fumera","year":"2002","unstructured":"Fumera, G., Roli, F.: Support vector machines with embedded reject option. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 68\u201382. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45665-1_6"},{"issue":"12","key":"38_CR10","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.1016\/S0031-3203(00)00059-5","volume":"33","author":"G Fumera","year":"2000","unstructured":"Fumera, G., Roli, F., Giacinto, G.: Reject option with multiple thresholds. Pattern Recogn. 33(12), 2099\u20132101 (2000)","journal-title":"Pattern Recogn."},{"key":"38_CR11","unstructured":"Geifman, Y., El-Yaniv, R.: Selective classification for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 4878\u20134887 (2017)"},{"key":"38_CR12","unstructured":"Geifman, Y., El-Yaniv, R.: Selectivenet: a deep neural network with an integrated reject option. arXiv preprint arXiv:1901.09192 (2019)"},{"key":"38_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1007\/978-3-030-32226-7_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"FC Ghesu","year":"2019","unstructured":"Ghesu, F.C., et al.: Quantifying and leveraging classification uncertainty for chest radiograph assessment. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 676\u2013684. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_75"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"38_CR15","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/TSSC.1970.300339","volume":"6","author":"ME Hellman","year":"1970","unstructured":"Hellman, M.E.: The nearest neighbor classification rule with a reject option. IEEE Trans. Syst. Sci. Cybern. 6(3), 179\u2013185 (1970)","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"issue":"5","key":"38_CR16","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018)","journal-title":"Cell"},{"issue":"7553","key":"38_CR17","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"38_CR18","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems, pp. 3179\u20133189 (2018)"},{"key":"38_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/978-3-030-32226-7_55","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Tardy","year":"2019","unstructured":"Tardy, M., Scheffer, B., Mateus, D.: Uncertainty measurements for the reliable classification of mammograms. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 495\u2013503. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_55"},{"key":"38_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/978-3-030-35514-2_27","volume-title":"Scalable Uncertainty Management","author":"Z Tong","year":"2019","unstructured":"Tong, Z., Xu, P., Den\u0153ux, T.: ConvNet and Dempster-Shafer theory for object recognition. In: Ben Amor, N., Quost, B., Theobald, M. (eds.) SUM 2019. LNCS (LNAI), vol. 11940, pp. 368\u2013381. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-35514-2_27"},{"issue":"3","key":"38_CR21","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ins.2009.09.021","volume":"180","author":"Y Yao","year":"2010","unstructured":"Yao, Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341\u2013353 (2010)","journal-title":"Inf. Sci."},{"key":"38_CR22","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.ins.2018.07.065","volume":"507","author":"X Yue","year":"2020","unstructured":"Yue, X., Chen, Y., Miao, D., Fujita, H.: Fuzzy neighborhood covering for three-way classification. Inf. Sci. 507, 795\u2013808 (2020)","journal-title":"Inf. Sci."},{"key":"38_CR23","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.ijar.2016.11.010","volume":"83","author":"X Yue","year":"2017","unstructured":"Yue, X., Chen, Y., Miao, D., Qian, J.: Tri-partition neighborhood covering reduction for robust classification. Int. J. Approximate Reasoning 83, 371\u2013384 (2017)","journal-title":"Int. J. Approximate Reasoning"},{"issue":"5","key":"38_CR24","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/TFUZZ.2020.2979365","volume":"28","author":"X Yue","year":"2020","unstructured":"Yue, X., Zhou, J., Yao, Y., Miao, D.: Shadowed neighborhoods based on fuzzy rough transformation for three-way classification. IEEE Trans. Fuzzy Syst. 28(5), 978\u2013991 (2020)","journal-title":"IEEE Trans. Fuzzy Syst."}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-55393-7_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:02:06Z","timestamp":1710248526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-55393-7_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030553920","9783030553937"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-55393-7_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"20 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ksem2020.org\/","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":"291","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":"58","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":"27","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":"20% - 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":"8","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}