{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:29:42Z","timestamp":1743092982998,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442063"},{"type":"electronic","value":"9783031442070"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44207-0_3","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T14:03:51Z","timestamp":1695305031000},"page":"26-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Model Based on\u00a0Samples Difficulty for\u00a0Imbalanced Data Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8584-6088","authenticated-orcid":false,"given":"Ao","family":"Shan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8704-9821","authenticated-orcid":false,"given":"Yeh-Ching","family":"Chung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"2","key":"3_CR1","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2012","unstructured":"Barua, S., Islam, M.M., Yao, X., Murase, K.: Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405\u2013425 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"3_CR2","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s10044-016-0583-6","volume":"21","author":"Z Borsos","year":"2018","unstructured":"Borsos, Z., Lemnaru, C., Potolea, R.: Dealing with overlap and imbalance: a new metric and approach. Pattern Anal. Appl. 21(2), 381\u2013395 (2018)","journal-title":"Pattern Anal. Appl."},{"issue":"8","key":"3_CR3","doi-asserted-by":"publisher","first-page":"2857","DOI":"10.1109\/TNNLS.2019.2914471","volume":"31","author":"LA Bugnon","year":"2019","unstructured":"Bugnon, L.A., Yones, C., Milone, D.H., Stegmayer, G.: Deep neural architectures for highly imbalanced data in bioinformatics. IEEE Trans. Neural Netw. Learn. Syst. 31(8), 2857\u20132867 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/978-3-642-40319-4_39","volume-title":"Trends and Applications in Knowledge Discovery and Data Mining","author":"P Cao","year":"2013","unstructured":"Cao, P., Zhao, D., Za\u00efane, O.R.: A PSO-based cost-sensitive neural network for imbalanced data classification. In: Li, J., et al. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7867, pp. 452\u2013463. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40319-4_39"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artifi. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artifi. Intell. Res."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TKDE.2014.2324567","volume":"27","author":"B Das","year":"2014","unstructured":"Das, B., Krishnan, N.C., Cook, D.J.: Racog and wracog: two probabilistic oversampling techniques. IEEE Trans. Knowl. Data Eng. 27(1), 222\u2013234 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Fernando, K.R.M., Tsokos, C.P.: Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2020.3047335"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328. IEEE (2008)","DOI":"10.1109\/IJCNN.2008.4633969"},{"issue":"9","key":"3_CR10","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Hu, Y., Zhang, Y., Gong, D., Sun, X.: Multi-participant federated feature selection algorithm with particle swarm optimizaiton for imbalanced data under privacy protection. IEEE Trans. Artifi. Intell. (2022)","DOI":"10.1109\/TAI.2022.3145333"},{"issue":"1","key":"3_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1\u201354 (2019)","journal-title":"J. Big Data"},{"issue":"4","key":"3_CR13","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Progress Artifi. Intell. 5(4), 221\u2013232 (2016)","journal-title":"Progress Artifi. Intell."},{"issue":"1","key":"3_CR14","first-page":"559","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(1), 559\u2013563 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Self-paced ensemble for highly imbalanced massive data classification. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 841\u2013852. IEEE (2020)","DOI":"10.1109\/ICDE48307.2020.00078"},{"key":"3_CR17","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(2) (2009)"},{"issue":"9","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1109\/TKDE.2017.2761347","volume":"30","author":"X Yang","year":"2017","unstructured":"Yang, X., Kuang, Q., Zhang, W., Zhang, G.: Amdo: an over-sampling technique for multi-class imbalanced problems. IEEE Trans. Knowl. Data Eng. 30(9), 1672\u20131685 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wang, R., Lei, Y., Liao, W.H., Cao, H., Cao, J.: Severity level diagnosis of parkinson\u2019s disease by ensemble k-nearest neighbor under imbalanced data. Expert Syst. Appli. 189, 116113 (2022)","DOI":"10.1016\/j.eswa.2021.116113"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44207-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T16:19:49Z","timestamp":1730132389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44207-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442063","9783031442070"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44207-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","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":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted : 9 Abstract","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)"}}]}}