{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:08:05Z","timestamp":1777900085371,"version":"3.51.4"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032060952","type":"print"},{"value":"9783032060969","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-06096-9_4","type":"book-chapter","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:54:10Z","timestamp":1758880450000},"page":"59-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GBRF: A Novel Framework for\u00a0Encoding User-Preferences in\u00a0Imbalanced Data Distributions via\u00a0Genetic Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9906-9128","authenticated-orcid":false,"given":"Miguel","family":"Carvalho","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9164-0016","authenticated-orcid":false,"given":"Armando","family":"Pinho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-9219","authenticated-orcid":false,"given":"Susana","family":"Br\u00e1s","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Alam, T., Qamar, S., Dixit, A., Benaida, M.: Genetic algorithm: reviews, implementations, and applications (2020). https:\/\/arxiv.org\/abs\/2007.12673","DOI":"10.36227\/techrxiv.12657173"},{"key":"4_CR2","unstructured":"Alimohammadi, D., Bolin, M.: Mathematics for classical information retrieval: roots and applications (2010)"},{"key":"4_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/978-3-642-37456-2_27","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"S Barua","year":"2013","unstructured":"Barua, S., Islam, M.M., Murase, K.: ProWSyn: proximity weighted synthetic oversampling technique for imbalanced data set learning. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 317\u2013328. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37456-2_27"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. 49(2) (2016). https:\/\/doi.org\/10.1145\/2907070","DOI":"10.1145\/2907070"},{"issue":"5","key":"4_CR5","doi-asserted-by":"publisher","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","volume":"80","author":"S Katoch","year":"2020","unstructured":"Katoch, S., Chauhan, S.S., Kumar, V.: A review on genetic algorithm: past, present, and future. Multimedia Tools Appl. 80(5), 8091\u20138126 (2020). https:\/\/doi.org\/10.1007\/s11042-020-10139-6","journal-title":"Multimedia Tools Appl."},{"key":"4_CR6","doi-asserted-by":"publisher","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","DOI":"10.1613\/jair.953"},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Douzas, G., Ba\u00e7\u00e3o, F., Last, F.: Improving imbalanced learning through a heuristic oversampling method based on k-means and smote. Inf. Sci. 465 (2018). https:\/\/doi.org\/10.1016\/j.ins.2018.06.056","DOI":"10.1016\/j.ins.2018.06.056"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Drown, D., Khoshgoftaar, T., Seliya, N.: Evolutionary sampling and software quality modeling of high-assurance systems. IEEE Trans. Syst. Man Cybern. Part A 39, 1097\u20131107 (2009). https:\/\/doi.org\/10.1109\/TSMCA.2009.2020804","DOI":"10.1109\/TSMCA.2009.2020804"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez, A., Garcia, S., Herrera, F., Chawla, N.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863\u2013905 (2018). https:\/\/doi.org\/10.1613\/jair.1.11192","DOI":"10.1613\/jair.1.11192"},{"key":"4_CR10","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets (2018). https:\/\/doi.org\/10.1007\/978-3-319-98074-4","DOI":"10.1007\/978-3-319-98074-4"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"Ha, J., Lee, J.S.: A new under-sampling method using genetic algorithm for imbalanced data classification. In: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication. IMCOM \u201916. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2857546.2857643","DOI":"10.1145\/2857546.2857643"},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328 (2008). https:\/\/doi.org\/10.1109\/IJCNN.2008.4633969","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"4_CR13","doi-asserted-by":"publisher","unstructured":"Jain, A., Ratnoo, S., Kumar, D.: Addressing class imbalance problem in medical diagnosis: a genetic algorithm approach, pp.\u00a01\u20138 (2017). https:\/\/doi.org\/10.1109\/ICOMICON.2017.8279150","DOI":"10.1109\/ICOMICON.2017.8279150"},{"issue":"8","key":"4_CR14","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.1007\/s13369-016-2179-2","volume":"41","author":"K Jiang","year":"2016","unstructured":"Jiang, K., Lu, J., Xia, K.: A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE. Arab. J. Sci. Eng. 41(8), 3255\u20133266 (2016). https:\/\/doi.org\/10.1007\/s13369-016-2179-2","journal-title":"Arab. J. Sci. Eng."},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Kim, H.J., Jo, N.O., Shin, K.S.: Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction. Expert Syst. Appl. 59(C), 226\u2013234 (2016). https:\/\/doi.org\/10.1016\/j.eswa.2016.04.027","DOI":"10.1016\/j.eswa.2016.04.027"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Kov\u00e1cs, G.: An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Appl. Soft Comput. (2019). https:\/\/doi.org\/10.1016\/j.asoc.2019.105662","DOI":"10.1016\/j.asoc.2019.105662"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Kulkarni, A., Chong, D., Batarseh, F.A.: 5 - foundations of data imbalance and solutions for a data democracy. In: Batarseh, F.A., Yang, R. (eds.) Data Democracy, pp. 83\u2013106. Academic Press (2020). https:\/\/doi.org\/10.1016\/B978-0-12-818366-3.00005-8. https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780128183663000058","DOI":"10.1016\/B978-0-12-818366-3.00005-8"},{"key":"4_CR18","unstructured":"Lu, Y., Cheung, Y.M., Tang, Y.Y.: Bayes imbalance impact index: a measure of class imbalanced dataset for classification problem (2019). https:\/\/arxiv.org\/abs\/1901.10173"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Onan, A., Garc\u00eda-D\u00edaz, V.: Consensus clustering-based undersampling approach to imbalanced learning. Sci. Program. 2019 (2019). https:\/\/doi.org\/10.1155\/2019\/5901087","DOI":"10.1155\/2019\/5901087"},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Santos, M.S., Abreu, P.H., Japkowicz, N., Fern\u00e1ndez, A., Santos, J.: A unifying view of class overlap and imbalance: key concepts, multi-view panorama, and open avenues for research. Inf. Fusion 89, 228\u2013253 (2023). https:\/\/doi.org\/10.1016\/j.inffus.2022.08.017. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253522001099","DOI":"10.1016\/j.inffus.2022.08.017"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Wernernbsp;denbsp;Vargas, V., Schneidernbsp;Aranda, J.A., dos Santosnbsp;Costa, R., da\u00a0Silvanbsp;Pereira, P.R., Vict\u00f3rianbsp;Barbosa, J.L.: Imbalanced data preprocessing techniques for machine learning: a systematic mapping study. Knowl. Inf. Syst. 65(1), 31\u201357 (2022). https:\/\/doi.org\/10.1007\/s10115-022-01772-8","DOI":"10.1007\/s10115-022-01772-8"},{"issue":"1","key":"4_CR22","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"D Wolpert","year":"1997","unstructured":"Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67\u201382 (1997). https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06096-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:23:24Z","timestamp":1777631004000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06096-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,27]]},"ISBN":["9783032060952","9783032060969"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06096-9_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,27]]},"assertion":[{"value":"27 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare\u00a0that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}