{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:16:49Z","timestamp":1774541809301,"version":"3.50.1"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030923068","type":"print"},{"value":"9783030923075","type":"electronic"}],"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\/978-3-030-92307-5_52","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T14:04:20Z","timestamp":1638799460000},"page":"447-455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CSMOTE: Contrastive Synthetic Minority Oversampling for\u00a0Imbalanced Time Series Classification"],"prefix":"10.1007","author":[{"given":"Pin","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xiaohui","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pengpeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Wo","sequence":"additional","affiliation":[]},{"given":"Xudong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"52_CR1","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: SPO: structure preserving oversampling for imbalanced time series classification. In: ICDM, pp. 1008\u20131013. IEEE (2011)","DOI":"10.1109\/ICDM.2011.137"},{"key":"52_CR2","doi-asserted-by":"publisher","first-page":"114463","DOI":"10.1016\/j.eswa.2020.114463","volume":"169","author":"VA Fajardo","year":"2021","unstructured":"Fajardo, V.A., et al.: On oversampling imbalanced data with deep conditional generative models. Expert Syst. Appl. 169, 114463 (2021)","journal-title":"Expert Syst. Appl."},{"key":"52_CR3","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez, A., et al.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. JAIR 61, 863\u2013905 (2018)","journal-title":"JAIR"},{"issue":"9","key":"52_CR4","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. Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"Trans. Knowl. Data Eng."},{"key":"52_CR5","unstructured":"He, H., et al.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IJCNN, pp. 1322\u20131328. IEEE (2008)"},{"issue":"2","key":"52_CR6","first-page":"263","volume":"6","author":"S Miri Rostami","year":"2018","unstructured":"Miri Rostami, S., et al.: Extracting predictor variables to construct breast cancer survivability model with class imbalance problem. J. AI Data Min. 6(2), 263\u2013276 (2018)","journal-title":"J. AI Data Min."},{"key":"52_CR7","unstructured":"Santurkar, S., Schmidt, L., Madry, A.: A classification-based study of covariate shift in GAN distributions. In: ICML, pp. 4480\u20134489. PMLR (2018)"},{"key":"52_CR8","doi-asserted-by":"crossref","unstructured":"Sharma, S., et al.: Synthetic oversampling with the majority class: a new perspective on handling extreme imbalance. In: ICDM, pp. 447\u2013456. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00060"},{"issue":"5","key":"52_CR9","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2014","unstructured":"Wang, S., et al.: Resampling-based ensemble methods for online class imbalance learning. IEEE Trans. Knowl. Data Eng. 27(5), 1356\u20131368 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"52_CR10","doi-asserted-by":"crossref","unstructured":"Wen, Q., et al.: Time series data augmentation for deep learning: a survey. In: IJCAI, pp. 4653\u20134660 (2021)","DOI":"10.24963\/ijcai.2021\/631"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92307-5_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T14:36:32Z","timestamp":1638801392000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92307-5_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030923068","9783030923075"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92307-5_52","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.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":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Due to the COVID-19 pandemic the conference was held online.","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)"}}]}}