{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:59:36Z","timestamp":1777705176615,"version":"3.51.4"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319157016","type":"print"},{"value":"9783319157023","type":"electronic"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"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":[[2015]]},"DOI":"10.1007\/978-3-319-15702-3_37","type":"book-chapter","created":{"date-parts":[[2015,3,16]],"date-time":"2015-03-16T10:36:18Z","timestamp":1426502178000},"page":"377-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique"],"prefix":"10.1007","author":[{"given":"Maciej","family":"Zi\u0119ba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jakub M.","family":"Tomczak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Gonczarek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,3,17]]},"reference":[{"key":"37_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-642-01307-2_43","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"C Bunkhumpornpat","year":"2009","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the classimbalanced problem. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 475\u2013482. Springer, Heidelberg (2009)"},{"key":"37_CR2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321\u2013357 (2002)","journal-title":"Journal of Artificial Intelligence Research"},{"key":"37_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-540-39804-2_12","volume-title":"Knowledge Discovery in Databases: PKDD 2003","author":"NV Chawla","year":"2003","unstructured":"Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavra\u010d, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107\u2013119. Springer, Heidelberg (2003)"},{"issue":"10","key":"37_CR4","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TNN.2010.2066988","volume":"21","author":"S Chen","year":"2010","unstructured":"Chen, S., He, H., Garcia, E.: RAMOBoost: Ranked minority oversampling in boosting. IEEE Transactions on Neural Networks 21(10), 1624\u20131642 (2010)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Ertekin, S., Huang, J., Giles, C.: Active learning for class imbalance problem. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 823\u2013824. ACM (2007)","DOI":"10.1145\/1277741.1277927"},{"issue":"4","key":"37_CR6","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1016\/j.asoc.2009.04.004","volume":"9","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda, S., Fern\u00e1ndez, A., Herrera, F.: Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems. Applied Soft Computing 9(4), 1304\u20131314 (2009)","journal-title":"Applied Soft Computing"},{"issue":"9","key":"37_CR7","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"5\u20136","key":"37_CR8","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1002\/sam.10061","volume":"2","author":"S Hido","year":"2009","unstructured":"Hido, S., Kashima, H., Takahashi, Y.: Roughly balanced bagging for imbalanced data. Statistical Analysis and Data Mining 2(5\u20136), 412\u2013426 (2009)","journal-title":"Statistical Analysis and Data Mining"},{"issue":"1","key":"37_CR9","first-page":"926","volume":"9","author":"G Hinton","year":"2010","unstructured":"Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)","journal-title":"Momentum"},{"key":"37_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-3-642-35289-8_32","volume-title":"Neural Networks: Tricks of the Trade","author":"GE Hinton","year":"2012","unstructured":"Hinton, G.E.: A practical guide to training restricted boltzmann machines. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 599\u2013619. Springer, Heidelberg (2012)"},{"key":"37_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005)"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Larochelle, H., Bengio, Y.: Classification using discriminative restricted boltzmann machines. In: Proceedings of the 25th International Conference on Machine Learning, pp. 536\u2013543. ACM (2008)","DOI":"10.1145\/1390156.1390224"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Maciejewski, T., Stefanowski, J.: Local neighbourhood extension of smote for mining imbalanced data. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 104\u2013111. IEEE (2011)","DOI":"10.1109\/CIDM.2011.5949434"},{"key":"37_CR14","unstructured":"Mani, J., Zhang, I.: KNN approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of International Conference on Machine Learning, Workshop Learning from Imbalanced Data Sets (2003)"},{"issue":"2","key":"37_CR15","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-011-0465-6","volume":"33","author":"E Ramentol","year":"2012","unstructured":"Ramentol, E., Caballero, Y., Bello, R., Herrera, F.: Smote-rsb*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory. Knowledge and Information Systems 33(2), 245\u2013265 (2012)","journal-title":"Knowledge and Information Systems"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"S\u00e1ez, J.A., Luengo, J., Stefanowski, J., Herrera, F.: SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences (2014)","DOI":"10.1016\/j.ins.2014.08.051"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791\u2013798. ACM (2007)","DOI":"10.1145\/1273496.1273596"},{"issue":"1","key":"37_CR18","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2010","unstructured":"Seiffert, C., Khoshgoftaar, T., Van Hulse, J., Napolitano, A.: RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(1), 185\u2013197 (2010)","journal-title":"IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans"},{"issue":"3","key":"37_CR19","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/TCBB.2007.70224","volume":"4","author":"Y Tang","year":"2007","unstructured":"Tang, Y., Zhang, Y., Huang, Z.: Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis. IEEE\/ACM Transactions on Computational Biology and Bioinformatics 4(3), 365\u2013381 (2007)","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"7","key":"37_CR20","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TPAMI.2006.134","volume":"28","author":"D Tao","year":"2006","unstructured":"Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088\u20131099 (2006)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"11","key":"37_CR21","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1109\/TSMC.1976.4309452","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek, I.: Two Modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics 6(11), 769\u2013772 (1976)","journal-title":"IEEE Transactions on Systems, Man and Cybernetics"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 324\u2013331. IEEE (2009)","DOI":"10.1109\/CIDM.2009.4938667"}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-15702-3_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T22:40:17Z","timestamp":1676932817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-15702-3_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319157016","9783319157023"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-15702-3_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]},"assertion":[{"value":"17 March 2015","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}