{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:07:29Z","timestamp":1765544849393,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001663","name":"Volkswagen Foundation","doi-asserted-by":"publisher","award":["BIAS"],"award-info":[{"award-number":["BIAS"]}],"id":[{"id":"10.13039\/501100001663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10115-022-01723-3","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T01:38:22Z","timestamp":1658972302000},"page":"2737-2770","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Parity-based cumulative fairness-aware boosting"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3005-4507","authenticated-orcid":false,"given":"Vasileios","family":"Iosifidis","sequence":"first","affiliation":[]},{"given":"Arjun","family":"Roy","sequence":"additional","affiliation":[]},{"given":"Eirini","family":"Ntoutsi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"1723_CR1","unstructured":"J. United States. Podesta (2014) Big data: seizing opportunities, preserving values. White House, Executive Office of the President"},{"key":"1723_CR2","unstructured":"Ingold D, Soper S (2016) Amazon doesn\u2019t consider the race of its customers. Should it. Bloomberg, April"},{"issue":"1","key":"1723_CR3","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1515\/popets-2015-0007","volume":"2015","author":"A Datta","year":"2015","unstructured":"Datta A, Tschantz MC, Datta A (2015) Automated experiments on ad privacy settings. Priv Enhancing Technol 2015(1):92\u2013112","journal-title":"Priv Enhancing Technol"},{"key":"1723_CR4","doi-asserted-by":"crossref","unstructured":"Edelman BG, Luca M (2014) Digital discrimination: the case of airbnb.com","DOI":"10.2139\/ssrn.2377353"},{"key":"1723_CR5","doi-asserted-by":"crossref","unstructured":"Sweeney L (2013) Discrimination in online ad delivery. arXiv preprint arXiv:1301.6822","DOI":"10.2139\/ssrn.2208240"},{"key":"1723_CR6","unstructured":"Larson J, Mattu S, Kirchner L, Angwin J (2016) How we analyzed the compas recidivism algorithm. ProPublica (5 2016) 9"},{"key":"1723_CR7","doi-asserted-by":"crossref","unstructured":"Krasanakis E, Xioufis ES, Papadopoulos S, Kompatsiaris Y (2018) Adaptive sensitive reweighting to mitigate bias in fairness-aware classification. In: Proceedings of the 2018 world wide web conference on world wide web, WWW 2018, Lyon, France, April 23\u201327, 2018. ACM, pp 853\u2013862","DOI":"10.1145\/3178876.3186133"},{"key":"1723_CR8","doi-asserted-by":"crossref","unstructured":"Zafar MB, Valera I, Gomez Rodriguez M, Gummadi KP (2017) Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In: Proceedings of the 26th international conference on world wide web. WWW, pp 1171\u20131180","DOI":"10.1145\/3038912.3052660"},{"key":"1723_CR9","doi-asserted-by":"crossref","unstructured":"Calders T, Kamiran F, Pechenizkiy M (2009) Building classifiers with independency constraints. In: 2009 IEEE ICDM workshops. IEEE, pp\u00a013\u201318","DOI":"10.1109\/ICDMW.2009.83"},{"key":"1723_CR10","unstructured":"Calmon FP, Wei D, Vinzamuri B, Ramamurthy KN, Varshney KR (2017) Optimized pre-processing for discrimination prevention. In: Proceedings of the 31st international conference on neural information processing systems, pp\u00a03995\u20134004"},{"issue":"1","key":"1723_CR11","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 (2012) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1\u201333","journal-title":"Knowl Inf Syst"},{"key":"1723_CR12","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. In: Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R (eds) Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, December 5\u201310, 2016, Barcelona, Spain, pp 3315\u20133323"},{"key":"1723_CR13","doi-asserted-by":"crossref","unstructured":"Fish B, Kun J, Lelkes \u00c1D (2016) A confidence-based approach for balancing fairness and accuracy. In: Proceedings of the 2016 SIAM international conference on data mining. SIAM, pp 144\u2013152","DOI":"10.1137\/1.9781611974348.17"},{"key":"1723_CR14","doi-asserted-by":"crossref","unstructured":"Kamiran F, Calders T (2009) Classifying without discriminating. In: Computer, control and communication. IEEE, pp 1\u20136","DOI":"10.1109\/IC4.2009.4909197"},{"key":"1723_CR15","doi-asserted-by":"crossref","unstructured":"Kamiran F, Calders T, Pechenizkiy M (2010) Discrimination aware decision tree learning. In: 2010 IEEE 10th international conference on data mining (ICDM). IEEE, pp 869\u2013874","DOI":"10.1109\/ICDM.2010.50"},{"issue":"4","key":"1723_CR16","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(4):463\u2013484","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"key":"1723_CR17","unstructured":"Iosifidis V (2020) Semi-supervised learning and fairness-aware learning under class imbalance. Ph.D. thesis, Hannover: Institutionelles Repositorium der Leibniz Universit\u00e4t Hannover"},{"issue":"4","key":"1723_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):e1249","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"1723_CR19","doi-asserted-by":"publisher","DOI":"10.1142\/11325","volume-title":"Ensemble learning: pattern classification using ensemble methods","author":"L Rokach","year":"2019","unstructured":"Rokach L (2019) Ensemble learning: pattern classification using ensemble methods. World Scientific, Singapore"},{"key":"1723_CR20","doi-asserted-by":"crossref","unstructured":"Iosifidis V, Ntoutsi E (2019) Adafair: cumulative fairness adaptive boosting. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 781\u2013790","DOI":"10.1145\/3357384.3357974"},{"key":"1723_CR21","doi-asserted-by":"crossref","unstructured":"Iosifidis V, Fetahu B, Ntoutsi E (2019) Fae: a fairness-aware ensemble framework. In: 2019 IEEE international conference on big data (big data). IEEE, pp 1375\u20131380","DOI":"10.1109\/BigData47090.2019.9006487"},{"issue":"8","key":"1723_CR22","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1177\/0956797615586188","volume":"26","author":"M Sch\u00e4fer","year":"2015","unstructured":"Sch\u00e4fer M, Haun DB, Tomasello M (2015) Fair is not fair everywhere. Psychol Sci 26(8):1252\u20131260","journal-title":"Psychol Sci"},{"key":"1723_CR23","doi-asserted-by":"crossref","unstructured":"Verma S, Rubin J (2018) Fairness definitions explained. In: Proceedings of the international workshop on software fairness, FairWare@ICSE 2018, Gothenburg, Sweden, May 29, 2018. ACM, pp 1\u20137","DOI":"10.1145\/3194770.3194776"},{"issue":"3","key":"1723_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1356","volume":"10","author":"E Ntoutsi","year":"2020","unstructured":"Ntoutsi E, Fafalios P, Gadiraju U, Iosifidis V, Nejdl W, Vidal M-E, Ruggieri S, Turini F, Papadopoulos S, Krasanakis E et al (2020) Bias in data-driven artificial intelligence systems-an introductory survey. Wiley Interdiscip Rev Data Min Knowl Discov 10(3):e1356","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"1723_CR25","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. ACM, pp 214\u2013226","DOI":"10.1145\/2090236.2090255"},{"issue":"6","key":"1723_CR26","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 (2021) A survey on bias and fairness in machine learning. ACM Comput Surv (CSUR) 54(6):1\u201335","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1723_CR27","unstructured":"Quy TL, Roy A, Iosifidis V, Ntoutsi E (2022) A survey on datasets for fairness-aware machine learning. WIREs Data Min Knowl Discov"},{"key":"1723_CR28","unstructured":"Iosifidis V, Ntoutsi E (2018) Dealing with bias via data augmentation in supervised learning scenarios. Jo Bates Paul D. Clough Robert J\u00e4schke, p 24"},{"key":"1723_CR29","doi-asserted-by":"crossref","unstructured":"Hu H, Iosifidis V, Liao W, Zhang H, YingYang M, Ntoutsi E, Rosenhahn B (2020) Fairnn-conjoint learning of fair representations for fair decisions. Discov Sci","DOI":"10.1007\/978-3-030-61527-7_38"},{"key":"1723_CR30","doi-asserted-by":"crossref","unstructured":"Iosifidis V, Tran TNH, Ntoutsi E (2019) Fairness-enhancing interventions in stream classification. In: Database and expert systems applications\u201430th international conference, DEXA 2019, Linz, Austria, August 26\u201329, 2019, proceedings, part I, vol 11706. Springer, pp 261\u2013276","DOI":"10.1007\/978-3-030-27615-7_20"},{"key":"1723_CR31","doi-asserted-by":"crossref","unstructured":"Zhang W, Ntoutsi E (2019) FAHT: an adaptive fairness-aware decision tree classifier. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, August 10\u201316, 2019, pp 1480\u20131486, ijcai.org","DOI":"10.24963\/ijcai.2019\/205"},{"key":"1723_CR32","doi-asserted-by":"crossref","unstructured":"Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Fairness-aware classifier with prejudice remover regularizer. In: European conference on principles of data mining and knowledge discovery. Springer, pp 35\u201350","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"1723_CR33","doi-asserted-by":"crossref","unstructured":"Iosifidis V, Ntoutsi E (2020) Fabboo\u2014online fairness-aware learning under class imbalance. In: International conference on discovery science. Springer, pp 159\u2013174","DOI":"10.1007\/978-3-030-61527-7_11"},{"key":"1723_CR34","unstructured":"Iosifidis V, Zhang W, Ntoutsi E (2021) Online fairness-aware learning with imbalanced data streams. arXiv preprint arXiv:2108.06231"},{"key":"1723_CR35","doi-asserted-by":"crossref","unstructured":"Pedreschi D, Ruggieri S, Turini F (2009) Measuring discrimination in socially-sensitive decision records. In: Proceedings of the SIAM international conference on data mining, SDM 2009, April 30\u2013May 2, 2009, Sparks, Nevada, USA. SIAM, pp 581\u2013592","DOI":"10.1137\/1.9781611972795.50"},{"issue":"2","key":"1723_CR36","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s10618-010-0190-x","volume":"21","author":"T Calders","year":"2010","unstructured":"Calders T, Verwer S (2010) Three naive Bayes approaches for discrimination-free classification. Data Min Knowl Discov 21(2):277\u2013292","journal-title":"Data Min Knowl Discov"},{"key":"1723_CR37","unstructured":"Pleiss G, Raghavan M, Wu F, Kleinberg J, Weinberger KQ (2017) On fairness and calibration. In: Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp 5680\u20135689"},{"key":"1723_CR38","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. In: 2010 20th international conference on pattern recognition. IEEE, pp 3121\u20133124","DOI":"10.1109\/ICPR.2010.764"},{"key":"#cr-split#-1723_CR39.1","unstructured":"Schapire RE (1999) A brief introduction to boosting. In: Dean T"},{"key":"#cr-split#-1723_CR39.2","unstructured":"(ed) Proceedings of the sixteenth international joint conference on artificial intelligence, IJCAI 99, Stockholm, Sweden, July 31- August 6, 1999, vol 2. Morgan Kaufmann, pp 1401-1406"},{"issue":"12","key":"1723_CR40","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun Y, Kamel MS, Wong AK, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit 40(12):3358\u20133378","journal-title":"Pattern Recognit"},{"issue":"3","key":"1723_CR41","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1023\/A:1007614523901","volume":"37","author":"RE Schapire","year":"1999","unstructured":"Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297\u2013336","journal-title":"Mach Learn"},{"key":"1723_CR42","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository"},{"key":"1723_CR43","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) Smoteboost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery. Springer, pp 107\u2013119","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"1723_CR44","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"1723_CR45","unstructured":"Roy A, Iosifidis V, Ntoutsi E (2021) Multi-fair Pareto boosting. arXiv preprint arXiv:2104.13312"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-022-01723-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-022-01723-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-022-01723-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T03:16:53Z","timestamp":1662952613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-022-01723-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,27]]},"references-count":46,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["1723"],"URL":"https:\/\/doi.org\/10.1007\/s10115-022-01723-3","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"type":"print","value":"0219-1377"},{"type":"electronic","value":"0219-3116"}],"subject":[],"published":{"date-parts":[[2022,7,27]]},"assertion":[{"value":"16 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}