{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:43:10Z","timestamp":1773888190858,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031490170","type":"print"},{"value":"9783031490187","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-49018-7_45","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:21Z","timestamp":1701039741000},"page":"630-645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DIF-SR: A Differential Item Functioning-Based Sample Reweighting Method"],"prefix":"10.1007","author":[{"given":"Diego","family":"Minatel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio R. S.","family":"Parmezan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariana","family":"C\u00fari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alneu","family":"de A. Lopes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"45_CR1","doi-asserted-by":"crossref","unstructured":"Amrieh, E.A., Hamtini, T., Aljarah, I.: Preprocessing and analyzing educational data set using X-API for improving student\u2019s performance. In: IEEE AEECT, pp. 1\u20135. IEEE (2015)","DOI":"10.1109\/AEECT.2015.7360581"},{"issue":"3","key":"45_CR2","first-page":"671","volume":"104","author":"S Barocas","year":"2016","unstructured":"Barocas, S., Selbst, A.D.: Big data\u2019s disparate impact. Cal. L. Rev. 104(3), 671\u2013732 (2016)","journal-title":"Cal. L. Rev."},{"issue":"4","key":"45_CR3","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/BF02293801","volume":"46","author":"RD Bock","year":"1981","unstructured":"Bock, R.D., Aitkin, M.: Marginal maximum likelihood estimation of item parameters: application of an EM algorithm. Psychometrika 46(4), 443\u2013459 (1981)","journal-title":"Psychometrika"},{"key":"45_CR4","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-031-21686-2_20","volume-title":"BRACIS 2022","author":"LF Cardoso","year":"2022","unstructured":"Cardoso, L.F., et al.: Explanation-by-example based on item response theory. In: Xavier-Junior, J.C., Rios, R.A. (eds.) BRACIS 2022. LNAI, vol. 13653, pp. 283\u2013297. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21686-2_20"},{"issue":"5","key":"45_CR5","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s11633-020-1239-y","volume":"17","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Ahn, H.: Item response theory based ensemble in machine learning. Int. J. Autom. Comput. 17(5), 621\u2013636 (2020)","journal-title":"Int. J. Autom. Comput."},{"key":"45_CR6","volume-title":"The Theory and Practice of Item Response Theory","author":"RJ De Ayala","year":"2013","unstructured":"De Ayala, R.J.: The Theory and Practice of Item Response Theory. Guilford Publications, New York (2013)"},{"key":"45_CR7","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: ITCS, pp. 214\u2013226. ACM (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"45_CR9","doi-asserted-by":"crossref","unstructured":"Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press (2013)","DOI":"10.4324\/9781410605269"},{"issue":"3","key":"45_CR10","first-page":"50","volume":"38","author":"B Goodman","year":"2017","unstructured":"Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a \u201cright to explanation\u2019\u2019. AI Mag. 38(3), 50\u201357 (2017)","journal-title":"AI Mag."},{"key":"45_CR11","volume-title":"Fundamentals of Item Response Theory","author":"RK Hambleton","year":"1991","unstructured":"Hambleton, R.K., Swaminathan, H., Rogers, H.J.: Fundamentals of Item Response Theory, vol. 2. SAGE Publications, Thousand Oaks (1991)"},{"key":"45_CR12","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: NIPS, pp. 3323\u20133331. Curran Associates, Inc. (2016)"},{"key":"45_CR13","unstructured":"Holland, P.W., Wainer, H.: Differential Item Functioning. Routledge (1993)"},{"key":"45_CR14","doi-asserted-by":"crossref","unstructured":"Hutchinson, B., Mitchell, M.: 50 years of test (un) fairness: lessons for machine learning. In: ACM FAT*, pp. 49\u201358. ACM (2019)","DOI":"10.1145\/3287560.3287600"},{"issue":"1","key":"45_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1\u201333 (2012). https:\/\/doi.org\/10.1007\/s10115-011-0463-8","journal-title":"Knowl. Inf. Syst."},{"key":"45_CR16","unstructured":"Kelly, M., Longjohn, R., Nottingham, K.: The UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"45_CR17","unstructured":"Larson, J., Mattu, S., Kirchner, L., Angwin, J.: How we analyzed the COMPAS recidivism algorithm (2016). https:\/\/www.propublica.org\/article\/how-we-analyzed-the-compas-recidivism-algorithm"},{"key":"45_CR18","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.artint.2018.09.004","volume":"271","author":"F Mart\u00ednez-Plumed","year":"2019","unstructured":"Mart\u00ednez-Plumed, F., Prud\u00eancio, R.B., Mart\u00ednez-Us\u00f3, A., Hern\u00e1ndez-Orallo, J.: Item response theory in AI: analysing machine learning classifiers at the instance level. Artif. Intell. 271, 18\u201342 (2019)","journal-title":"Artif. Intell."},{"key":"45_CR19","doi-asserted-by":"crossref","unstructured":"McNamara, D., Ong, C.S., Williamson, R.C.: Costs and benefits of fair representation learning. In: AAAI\/ACM AIES, pp. 263\u2013270. ACM (2019)","DOI":"10.1145\/3306618.3317964"},{"issue":"6","key":"45_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv."},{"key":"45_CR21","doi-asserted-by":"crossref","unstructured":"Minatel, D., dos Santos, N.R., da Silva, A.C.M., Curi, M., Marcacini, R.M., de Andrade Lopes, A.: Unfairness in machine learning for web systems applications. In: Proceedings of the Brazilian Symposium on Multimedia and the Web (2023)","DOI":"10.1145\/3617023.3617043"},{"key":"45_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2017.01.013","volume":"75","author":"ARS Parmezan","year":"2017","unstructured":"Parmezan, A.R.S., Lee, H.D., Wu, F.C.: Metalearning for choosing feature selection algorithms in data mining: proposal of a new framework. Expert Syst. Appl. 75, 1\u201324 (2017)","journal-title":"Expert Syst. Appl."},{"key":"45_CR23","unstructured":"Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., Weinberger, K.Q.: On fairness and calibration. In: NIPS, pp. 5680\u20135689. Curran Associates, Inc. (2017)"},{"key":"45_CR24","unstructured":"Podesta, J.: Big data: seizing opportunities, preserving values. White House, Executive Office of the President, Washington (2014)"},{"issue":"2","key":"45_CR25","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2013","unstructured":"Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. SIGKDD Explor. 15(2), 49\u201360 (2013)","journal-title":"SIGKDD Explor."},{"key":"45_CR26","doi-asserted-by":"crossref","unstructured":"Zafar, M.B., Valera, I., Gomez Rodriguez, M., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact: learning classification without disparate mistreatment. In: WWW, pp. 1171\u20131180. IW3C2 (2017)","DOI":"10.1145\/3038912.3052660"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49018-7_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:14:13Z","timestamp":1701040453000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49018-7_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9783031490170","9783031490187"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49018-7_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"27 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2023","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","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":"61","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":"58% - 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","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":"5","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)"}}]}}