{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T11:32:24Z","timestamp":1742643144384,"version":"3.37.3"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["106-2118-M-001 -007 -MY2"],"award-info":[{"award-number":["106-2118-M-001 -007 -MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Classif"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s00357-021-09388-3","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T06:02:36Z","timestamp":1620626556000},"page":"544-555","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Model-Free Subject Selection Method for Active Learning Classification Procedures"],"prefix":"10.1007","volume":"38","author":[{"given":"Bo-Shiang","family":"Ke","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4977-7721","authenticated-orcid":false,"given":"Yuan-chin Ivan","family":"Chang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,10]]},"reference":[{"key":"9388_CR1","volume-title":"An introduction to categorical data analysis","author":"A Agresti","year":"2018","unstructured":"Agresti, A. (2018). An introduction to categorical data analysis. New York: Wiley."},{"key":"9388_CR2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.knosys.2013.12.023","volume":"60","author":"B Antal","year":"2014","unstructured":"Antal, B., & Hajdu, A. (2014). An ensemble-based system for automatic screening of diabetic retinopathy. Knowledge-Based Systems, 60, 20\u201327.","journal-title":"Knowledge-Based Systems"},{"key":"9388_CR3","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.neucom.2018.11.036","volume":"329","author":"Y-CI Chang","year":"2019","unstructured":"Chang, Y.-C.I., & Chen, R.-B. (2019). Active learning with simultaneous subject and variable selections. Neurocomputing, 329, 495\u2013505.","journal-title":"Neurocomputing"},{"issue":"2","key":"9388_CR4","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1111\/biom.13160","volume":"76","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Wang, Z., & Chang, Y.-C.I. (2020). Sequential adaptive variables and subject selection for gee methods. Biometrics, 76(2), 496\u2013507.","journal-title":"Biometrics"},{"issue":"2","key":"9388_CR5","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/j.2517-6161.1986.tb01398.x","volume":"48","author":"RD Cook","year":"1986","unstructured":"Cook, R.D. (1986). Assessment of local influence. Journal of the Royal Statistical Society, Series B, 48(2), 133\u2013169.","journal-title":"Journal of the Royal Statistical Society, Series B"},{"issue":"487","key":"9388_CR6","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1198\/jasa.2009.ap07625","volume":"104","author":"X Deng","year":"2009","unstructured":"Deng, X., Joseph, V.R., Sudjianto, A., & Wu, C.J. (2009). Active learning through sequential design, with applications to detection of money laundering. Journal of the American Statistical Association, 104(487), 969\u2013981.","journal-title":"Journal of the American Statistical Association"},{"key":"9388_CR7","unstructured":"Dua, D., & Graff, C. (2017). UCI machine learning repository."},{"issue":"346","key":"9388_CR8","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1080\/01621459.1974.10482962","volume":"69","author":"FR Hampel","year":"1974","unstructured":"Hampel, F.R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346), 383\u2013393.","journal-title":"Journal of the American Statistical Association"},{"key":"9388_CR9","doi-asserted-by":"crossref","unstructured":"Owen, A.B. (2001). Empirical likelihood. CRC Press.","DOI":"10.1201\/9781420036152"},{"key":"9388_CR10","doi-asserted-by":"crossref","unstructured":"Pepe, M. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford University Press.","DOI":"10.1093\/oso\/9780198509844.001.0001"},{"issue":"1","key":"9388_CR11","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1111\/j.0006-341X.2004.00200.x","volume":"60","author":"MS Pepe","year":"2004","unstructured":"Pepe, M.S., & Cai, T. (2004). The analysis of placement values for evaluating discriminatory measures. Biometrics, 60(1), 528\u2013535.","journal-title":"Biometrics"},{"issue":"3","key":"9388_CR12","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s10994-007-5019-5","volume":"68","author":"AI Schein","year":"2007","unstructured":"Schein, A.I., & Ungar, L.H. (2007). Active learning for logistic regression: an evaluation. Machine Learning, 68(3), 235\u2013265.","journal-title":"Machine Learning"},{"issue":"Nov","key":"9388_CR13","first-page":"45","volume":"2","author":"S Tong","year":"2001","unstructured":"Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2(Nov), 45\u201366.","journal-title":"Journal of Machine Learning Research"},{"key":"9388_CR14","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.neucom.2016.10.013","volume":"222","author":"J Wang","year":"2017","unstructured":"Wang, J., & Park, E. (2017). Active learning for penalized logistic regression via sequential experimental design. Neurocomputing, 222, 183\u2013190.","journal-title":"Neurocomputing"}],"container-title":["Journal of Classification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-021-09388-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00357-021-09388-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-021-09388-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T13:44:41Z","timestamp":1725025481000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00357-021-09388-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,10]]},"references-count":14,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["9388"],"URL":"https:\/\/doi.org\/10.1007\/s00357-021-09388-3","relation":{},"ISSN":["0176-4268","1432-1343"],"issn-type":[{"type":"print","value":"0176-4268"},{"type":"electronic","value":"1432-1343"}],"subject":[],"published":{"date-parts":[[2021,5,10]]},"assertion":[{"value":"5 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}