Open Access

Evaluation of Resampling Methods in the Class Unbalance Problem

   | May 29, 2020

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Bolton R.J., Hand D.J., 2002, Statistical fraud detection, Statistical Science, vol. 17, no. 3, 235-255.10.1214/ss/1042727940Search in Google Scholar

Breiman L., 2001, Random forests, Machine Learning, 45, 5-32.10.1023/A:1010933404324Search in Google Scholar

Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., 2002, SMOTE: Synthetic Minority Over-sampling Technique, Journal of Artificial Intelligence Research, 16, 321-357.10.1613/jair.953Search in Google Scholar

Chawla N.V., Japkowicz N., Kołcz A., 2004, Special issue on learning from imbalanced data sets, ACM Sigkdd Explorations Newsletter, 6(1), 1-6.10.1145/1007730.1007733Search in Google Scholar

Chen C., Liaw A., Breiman L., 2004, Using Random Forest to Learn Imbalanced Data, University of California, Berkeley, 110, 1-12.Search in Google Scholar

Dua D., Graff C., 2019, UCI Machine Learning Repository, University of California,: School of Information and Computer Science, Irvine, CA http://archive.ics.uci.edu/mlSearch in Google Scholar

Estabrooks A., Jo T., Japkowicz N., 2004, A multiple resampling method for learning from imbalanced data sets, Computational Intelligence, 20(1), 18-36.10.1111/j.0824-7935.2004.t01-1-00228.xSearch in Google Scholar

Fawcett T., 2006, An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874.10.1016/j.patrec.2005.10.010Search in Google Scholar

Friedman J., Hastie T., Tibshirani R., 2008, Regularization paths for generalized linear models via coordinate descent, Technical report, Stanford University.Search in Google Scholar

Galar M., Fernandez A., Barrenechea E., Bustince H., Herrera F., 2011, A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.10.1109/TSMCC.2011.2161285Search in Google Scholar

Haixiang G., Yijing L., Shang J., Mingyun G., Yuanyue H., Bing G., 2017, Learning from class-imbalanced data: Review of methods and applications, Expert Systems with Applications, 73, 220-239.10.1016/j.eswa.2016.12.035Search in Google Scholar

Hastie T., Tibshirani R., Friedman J., 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, New York.10.1007/978-0-387-84858-7Search in Google Scholar

Japkowicz N., Shah M., 2011, Evaluating learning algorithms: a classification perspective, Cambridge University Press.10.1017/CBO9780511921803Search in Google Scholar

King G., Zeng L., 2001, Logistic regression in rare events data, Political Analysis, 9, 137-163.10.1093/oxfordjournals.pan.a004868Search in Google Scholar

Kumar N.S., Rao K.N., Govardhan A., Reddy K.S. & Mahmood A.M., 2014, Undersampled k-means approach for handling imbalanced distributed data, Progress in Artificial Intelligence, 3(1), 29-38.10.1007/s13748-014-0045-6Search in Google Scholar

Lee S., 2000, Noisy replication in skewed binary classification, Computational Statistics and Data Analysis, 34, 165-191.10.1016/S0167-9473(99)00095-XSearch in Google Scholar

Longadge R., Dongre S.S., Malik L., 2013, Class imbalance problem in data mining: review, International Journal of Computer Science and Network, vol. 2, issue 1, 83-87.Search in Google Scholar

Loyola-González O., Martínez-Trinidad J. F., Carrasco-Ochoa J.A., García-Borroto M., 2016, Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases, Neurocomputing, 175, 935-947.10.1016/j.neucom.2015.04.120Search in Google Scholar

López V., Fernández A., Moreno-Torres J. G., & Herrera F., 2012, Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics, Expert Systems with Applications, 39(7), 6585-6608.10.1016/j.eswa.2011.12.043Search in Google Scholar

Menardi G., Torelli N., 2014, Training and assessing classification rules with imbalanced data, Data Mining and Knowledge Discovery, 28, 92-122.10.1007/s10618-012-0295-5Search in Google Scholar

Misztal M., 2014, Wybrane metody oceny jakości klasyfikatorów – przegląd i przykłady zastosowań, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu nr 328, Taksonomia 23, Klasyfikacja i analiza danych – teoria i zastosowania, 156-166.Search in Google Scholar

Pociecha J., Pawełek B., Baryła M., Augustyn S., 2014, Statystyczne metody prognozowania bankructwa w zmieniającej się koniunkturze gospodarczej, Fundacja Uniwersytetu Ekonomicznego w Krakowie, Kraków.Search in Google Scholar

Weiss G., 2004, Mining with rarity: A unifying framework, SIGKDD Explorations, 6(1), 7-19.10.1145/1007730.1007734Search in Google Scholar

Zou H., Hastie T., 2005, Regularization and variable selection via the elastic net,, Journal of the Royal Statistical Society, Series B. 67(2), 301-320.10.1111/j.1467-9868.2005.00503.xSearch in Google Scholar

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