{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T15:06:57Z","timestamp":1769267217396,"version":"3.49.0"},"publisher-location":"Berlin, Heidelberg","reference-count":12,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783642329210","type":"print"},{"value":"9783642329227","type":"electronic"}],"license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-3-642-32922-7_31","type":"book-chapter","created":{"date-parts":[[2012,8,23]],"date-time":"2012-08-23T05:26:37Z","timestamp":1345699597000},"page":"297-306","source":"Crossref","is-referenced-by-count":2,"title":["Proposing a New Method for Non-relative Imbalanced Dataset"],"prefix":"10.1007","author":[{"given":"Hamid","family":"Parvin","sequence":"first","affiliation":[]},{"given":"Sara","family":"Ansari","sequence":"additional","affiliation":[]},{"given":"Sajad","family":"Parvin","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"issue":"9","key":"31_CR1","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 Trans. Knowledge And Data Engineering\u00a021(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowledge And Data Engineering"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory Under Sampling for Class Imbalance Learning. In: Proc. Int\u2019l Conf. Data Mining, pp. 965\u2013969 (2006)","DOI":"10.1109\/ICDM.2006.68"},{"key":"31_CR3","unstructured":"Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory Under sampling for Class-Imbalance Learning. IEEE Transactions on Systems, Man, and Cybernetics-part B: Cybernetics (2009)"},{"key":"31_CR4","unstructured":"Zhang, J., Mani, I.: KNN Approach to Imbalanced Data Distributions: A Case Study Involving Information Extraction. In: Int\u2019l Conf. Machine Learning (2003)"},{"key":"31_CR5","unstructured":"Hamzei, M., Kangavari, M.R.: Learning from imbalanced data. Technical Report, Iran University of Sci. & Tech., Iran (2010)"},{"key":"31_CR6","unstructured":"Minaei, F., Soleimanian, M., Kheirkhah, D.: Investigation the relationship between risk factors of occurrence of breast tumor in women, Aranobidgol, Iran (2009)"},{"key":"31_CR7","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N.V. Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-Sampling Technique. J. Artificial Intelligence Research\u00a016, 321\u2013357 (2002)","journal-title":"J. Artificial Intelligence Research"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. In: Proc. Int\u2019l J. Conf. Neural Networks, pp. 1322\u20131328 (2008)","DOI":"10.1109\/IJCNN.2008.4633969"},{"issue":"1","key":"31_CR9","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"G.E.A.P.A. Batista","year":"2004","unstructured":"Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. ACM SIGKDD Explorations Newsletter\u00a06(1), 20\u201329 (2004)","journal-title":"ACM SIGKDD Explorations Newsletter"},{"issue":"1","key":"31_CR10","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1007730.1007737","volume":"6","author":"T. Jo","year":"2004","unstructured":"Jo, T., Japkowicz, N.: Class Imbalances versus Small Disjuncts. ACM SIGKDD Explorations Newsletter\u00a06(1), 40\u201349 (2004)","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"31_CR11","series-title":"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":"N.V. 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.\u00a02838, pp. 107\u2013119. Springer, Heidelberg (2003)"},{"issue":"2","key":"31_CR12","first-page":"1971","volume":"5","author":"R.E. Schapire","year":"1990","unstructured":"Schapire, R.E.: The strength of weak learn ability. Machine Learning\u00a05(2), 1971\u20131227 (1990)","journal-title":"Machine Learning"}],"container-title":["Advances in Intelligent Systems and Computing","Soft Computing Models in Industrial and Environmental Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-32922-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T03:44:51Z","timestamp":1743997491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-642-32922-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"ISBN":["9783642329210","9783642329227"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-32922-7_31","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013]]}}}