{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:11:51Z","timestamp":1742933511635,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030750145"},{"type":"electronic","value":"9783030750152"}],"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\/978-3-030-75015-2_6","type":"book-chapter","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T21:11:27Z","timestamp":1620076287000},"page":"57-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators"],"prefix":"10.1007","author":[{"given":"Koji","family":"Tabata","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsuyoshi","family":"Nakumura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamiki","family":"Komatsuzaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"unstructured":"Audibert, J.Y., Bubeck, S.: Best arm identification in multi-armed bandits (2010)","key":"6_CR1"},{"unstructured":"Gabillon, V., Ghavamzadeh, M., Lazaric, A.: Best arm identification: a unified approach to fixed budget and fixed confidence. In: Advances in Neural Information Processing Systems, pp. 3212\u20133220 (2012)","key":"6_CR2"},{"unstructured":"Kalyanakrishnan, S., Tewari, A., Auer, P., Stone, P.: Pac subset selection in stochastic multi-armed bandits. In: ICML, vol. 12, pp. 655\u2013662 (2012)","key":"6_CR3"},{"issue":"5","key":"6_CR4","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s10994-019-05784-4","volume":"108","author":"H Kano","year":"2019","unstructured":"Kano, H., Honda, J., Sakamaki, K., Matsuura, K., Nakamura, A., Sugiyama, M.: Good arm identification via bandit feedback. Mach. Learn. 108(5), 721\u2013745 (2019). https:\/\/doi.org\/10.1007\/s10994-019-05784-4","journal-title":"Mach. Learn."},{"unstructured":"Kaufmann, E., Koolen, W.M., Garivier, A.: Sequential test for the lowest mean: from Thompson to murphy sampling. In: Advances in Neural Information Processing Systems, pp. 6332\u20136342 (2018)","key":"6_CR5"},{"unstructured":"Locatelli, A., Gutzeit, M., Carpentier, A.: An optimal algorithm for the thresholding bandit problem. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, vol. 48, pp. 1690\u20131698 (2016)","key":"6_CR6"},{"issue":"3","key":"6_CR7","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/24.103000","volume":"39","author":"JA Nachlas","year":"1990","unstructured":"Nachlas, J.A., Loney, S.R., Binney, B.A.: Diagnostic-strategy selection for series systems. IEEE Trans. Reliab. 39(3), 273\u2013280 (1990)","journal-title":"IEEE Trans. Reliab."},{"unstructured":"Pelissier, A., et al.: Intelligent measurement analysis on single cell Raman images for the diagnosis of follicular thyroid carcinoma. arXiv preprint (2019). arxiv.org\/abs\/1904.05675","key":"6_CR8"},{"issue":"4","key":"6_CR9","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1109\/3468.769753","volume":"29","author":"V Raghavan","year":"1999","unstructured":"Raghavan, V., Shakeri, M., Pattipati, K.: Test sequencing algorithms with unreliable tests. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Humans 29(4), 347\u2013357 (1999)","journal-title":"IEEE Trans. Syst. Man Cybern.-Part A: Syst. Humans"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-019-05854-7","volume":"109","author":"K Tabata","year":"2019","unstructured":"Tabata, K., Nakamura, A., Honda, J., Komatsuzaki, T.: A bad arm existence checking problem: how to utilize asymmetric problem structure? Mach. Learn. 109, 1\u201346 (2019). https:\/\/doi.org\/10.1007\/s10994-019-05854-7","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Trends and Applications in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75015-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T21:13:25Z","timestamp":1620076405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75015-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030750145","9783030750152"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75015-2_6","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":"3 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"23% - 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":"7","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)"}}]}}