{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:49:02Z","timestamp":1743007742666,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954697"},{"type":"electronic","value":"9783030954703"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-95470-3_30","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"395-407","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Active Learning for Capturing Human Decision Policies in a Data Frugal Context"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5827-8385","authenticated-orcid":false,"given":"Lo\u00efc","family":"Grosset\u00eate","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4127-4134","authenticated-orcid":false,"given":"Alexandre","family":"Marois","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9219-4564","authenticated-orcid":false,"given":"B\u00e9n\u00e9dicte","family":"Chatelais","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3697-4184","authenticated-orcid":false,"given":"Christian","family":"Gagn\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1669-353X","authenticated-orcid":false,"given":"Daniel","family":"Lafond","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"30_CR1","unstructured":"Settles, B.: Active Learning Literature Survey, vol. 52. University of Wisconsin Madison (2010)"},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1177\/1555343416661889","volume":"11","author":"D Lafond","year":"2017","unstructured":"Lafond, D., Roberge-Valli\u00e8res, B., Vachon, F., Tremblay, S.: Judgment analysis in a dynamic multitask environment: capturing nonlinear policies using decision trees. J. Cogn. Eng. Decis. Mak. 11, 122\u2013135 (2017)","journal-title":"J. Cogn. Eng. Decis. Mak."},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Labont\u00e9, K., Lafond, D., Hunter, A., Neyedli, H.F., Tremblay, S.: Comparing two decision support modes using the cognitive shadow online policy-capturing system. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 64, pp. 1125\u20131129 (2020)","DOI":"10.1177\/1071181320641270"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Lafond, D., Tremblay, S., Banbury, S.: Cognitive shadow: a policy capturing tool to support naturalistic decision making. Presented at the IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, 1 February 2013 (2013)","DOI":"10.1109\/CogSIMA.2013.6523837"},{"key":"30_CR5","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-25629-6_13","volume-title":"Human Interaction and Emerging Technologies","author":"D Lafond","year":"2020","unstructured":"Lafond, D., Labont\u00e9, K., Hunter, A., Neyedli, H.F., Tremblay, S.: Judgment analysis for real-time decision support using the cognitive shadow policy-capturing system. In: Ahram, T., Taiar, R., Colson, S., Choplin, A. (eds.) IHIET 2019. AISC, vol. 1018, pp. 78\u201383. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-25629-6_13"},{"key":"30_CR6","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-030-39512-4_28","volume-title":"Intelligent Human Systems Integration 2020","author":"B Chatelais","year":"2020","unstructured":"Chatelais, B., Lafond, D., Hains, A., Gagn\u00e9, C.: Improving policy-capturing with active learning for real-time decision support. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds.) IHSI 2020. AISC, vol. 1131, pp. 177\u2013182. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39512-4_28"},{"key":"30_CR7","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-0-306-47630-3_9","volume-title":"Principles of Forecasting","author":"JS Armstrong","year":"2001","unstructured":"Armstrong, J.S.: Judgmental bootstrapping: inferring experts\u2019 rules for forecasting. In: Armstrong, J.S. (ed.) Principles of Forecasting, pp. 171\u2013192. Springer, Boston (2001). https:\/\/doi.org\/10.1007\/978-0-306-47630-3_9"},{"key":"30_CR8","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1186\/s12859-018-2264-5","volume":"19","author":"R Couronn\u00e9","year":"2018","unstructured":"Couronn\u00e9, R., Probst, P., Boulesteix, A.-L.: Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics 19, 270 (2018)","journal-title":"BMC Bioinformatics"},{"key":"30_CR9","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"30_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1\u201340 (2016). https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J. Big Data"},{"key":"30_CR11","unstructured":"Yang, Y.-Y., Lee, S.-C., Chung, Y.-A., Wu, T.-E., Chen, S.-A., Lin, H.-T.: libact: pool-based active learning in python (2017). https:\/\/arxiv.org\/abs\/1710.00379"},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.neucom.2014.06.042","volume":"147","author":"L Wang","year":"2015","unstructured":"Wang, L., Hu, X., Yuan, B., Lu, J.: Active learning via query synthesis and nearest neighbour search. Neurocomputing 147, 426\u2013434 (2015)","journal-title":"Neurocomputing"},{"key":"30_CR13","unstructured":"Atlas, L., et al.: Training connectionist networks with queries and selective sampling. In: Proceedings of the 2nd International Conference on Neural Information Processing Systems, pp. 566\u2013573. MIT Press, Cambridge (1989)"},{"key":"30_CR14","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.ins.2014.04.034","volume":"285","author":"J Smailovi\u0107","year":"2014","unstructured":"Smailovi\u0107, J., Gr\u010dar, M., Lavra\u010d, N., \u017dnidar\u0161i\u010d, M.: Stream-based active learning for sentiment analysis in the financial domain. Inf. Sci. 285, 181\u2013203 (2014)","journal-title":"Inf. Sci."},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"106500","DOI":"10.1016\/j.knosys.2020.106500","volume":"210","author":"A Tharwat","year":"2020","unstructured":"Tharwat, A., Schenck, W.: Balancing exploration and exploitation: a novel active learner for imbalanced data. Knowl. Based Syst. 210, 106500 (2020)","journal-title":"Knowl. Based Syst."},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.eswa.2017.05.046","volume":"85","author":"M Wang","year":"2017","unstructured":"Wang, M., Min, F., Zhang, Z.-H., Wu, Y.-X.: Active learning through density clustering. Expert Syst. Appl. 85, 305\u2013317 (2017)","journal-title":"Expert Syst. Appl."},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory - COLT \u201992, pp. 287\u2013294. ACM Press, Pittsburgh (1992)","DOI":"10.1145\/130385.130417"},{"key":"30_CR18","unstructured":"Lewis, D., Catlett, J., Cohen, W., Hirsh, H.: Heterogeneous Uncertainty Sampling for Supervised Learning (1996)"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Cai, W., Zhang, Y., Zhou, J.: Maximizing expected model change for active learning in regression. In: 2013 IEEE 13th International Conference on Data Mining, pp. 51\u201360. IEEE, Dallas (2013)","DOI":"10.1109\/ICDM.2013.104"},{"key":"30_CR20","unstructured":"Roy, N., Mccallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th International Conference on Machine Learning (2001)"},{"key":"30_CR21","unstructured":"Danka, T., Horvath, P.: modAL: a modular active learning framework for python (2018). https:\/\/arxiv.org\/abs\/1805.00979"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Marois, A., Chatelais, B., Grosset\u00eate, L., Lafond, D.: Evaluation of evolutionary algorithms under frugal learning constraints for online policy capturing. Presented at the IEEE International Multi-disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), Conference Presented Virtually 21 April (2021)","DOI":"10.1109\/CogSIMA51574.2021.9475930"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Bouneffouf, D., Laroche, R., Urvoy, T., F\u00e9raud, R., Allesiardo, R.: Contextual Bandit for Active Learning: Active Thompson Sampling (2014). https:\/\/hal.archives-ouvertes.fr\/hal-01069802","DOI":"10.1007\/978-3-319-12637-1_51"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95470-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:16:33Z","timestamp":1643710593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95470-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954697","9783030954703"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95470-3_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"215","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":"86","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":"40% - 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":"5-6","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":"1-2","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)"}}]}}