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The Influence of Unbalanced Economic Data on Feature Selection and Quality of Classifiers

   | 20 ago 2020

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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.Search 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. Irvine, CA: University of California, School of Information and Computer Science. Retrieved from: http://archive.ics.uci.edu/ml (17.06.2019).Search 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

Fayyad, U., Irani, K. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (pp. 1022–1027).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.Search in Google Scholar

Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (2006). Feature Extraction: Foundations and Applications. New York: Springer.10.1007/978-3-540-35488-8Search in Google Scholar

Guyon, I., Weston, J., Barnhill, S., Vapnik, V. (2002). Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning, 46, 389–422.10.1023/A:1012487302797Search 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

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

Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF, Proceedings of European Conference on Machine Learning (pp. 171–182).Search in Google Scholar

Kubus, M. (2015). Rekurencyjna eliminacja cech w metodach dyskryminacji. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 384. Taksonomia, 24, 154–162. DOI: 10.15611/pn.2015.384.16.10.15611/pn.2015.384.16Search in Google Scholar

Kubus, M. (2016). Lokalna ocena mocy dyskryminacyjnej zmiennych. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 427, Taksonomia 27, 143–152. DOI: 10.15611/pn.2016.427.15.10.15611/pn.2016.427.15Search 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, 2 (1), 83–87.Search 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

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

Tomek, I. (1976). Two modifications of CNN. IEEE Trans. Systems, Man and Cybernetics, 6, 769–772.Search in Google Scholar

Tsamardinos, I., Aliferis, C.F. (2003). Towards principled feature selection: relevancy, filters and wrappers. In Proceedings of the Workshop on Artificial Intelligence and Statistics.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

Yu, L., Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, 1205–1224.Search 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

eISSN:
1898-0198
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Inglés
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2 veces al año
Temas de la revista:
Business and Economics, Political Economics, other