Abstract
The paper discusses the basic problems resulting from the classification of imbalanced data, which are additionally described by a large number of parameters. The paper also presents various optimization methods, including the use of a synthetic indicator that is the product of specificity and the power of sensitivity, which was proposed by the author.
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References
Armitage, P.: Metody statystyczne w badaniach medycznych. Państwowy Zakład Wydawnictw Lekarskich, Warszawa (1978)
Bator, M.: Automatyczna detekcja zmian nowotworowych w obrazach mammograficznych z wykorzystaniem dopasowania wzorców i wybranych narzędzi sztucznej inteligencji. Instytut Podstawowych Problemów Techniki PAN. Praca doktorska pod kierunkiem prof. dr hab.inż. Mariusz Jacek Nieniewski (2008)
Chawla, N.: Data mining for imbalanced datasets: an overview. In: Maimon, O. Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, Part 6, pp. 875–886 (2010)
Fawcett, T., Provost, F.: Adaptive fraud detection. J. Data Min. Knowl. Disc. 1(3), 291–316 (1997)
Fernández, A., García, S., Herrera, F.: Addressing the classification with imbalanced data: open problems and new challenges on class distribution. In: Hybrid Artificial Intelligent Systems. LNCS, vol. 6678, pp. 1–10 (2011)
García, V., Sánchez, J.S., Mollineda, R.A.: Exploring the performance of resampling strategies for the class imbalance problem. In: Trends in Applied Intelligent Systems. LNCS, vol. 6096, pp. 2010, pp.541–549 (2010)
Górecki, H.: Optymalizacja i sterowanie systemów dynamicznych”, Uczelniane Wydawnictwa Naukowo-Dydaktyczne Akademii Górniczo-Hutniczej w Krakowie, Kraków (2006)
Japkowicz, N., Myers, C., Gluck, M.: A novelty detection approach to classification. In: Proceedings of the Fourteenth Joint Conference on Artificial Intelligence, pp. 518–523 (1995)
Kubat, M., Holte, R.C., Matwin, S.: Machine Learning for the detection of oil spills in satellite radar images. J. Mach. Learn. (Special Issue on Applications of Machine Learning and the Knowledge Discovery Process) 30(2–3), 195–215 (1998)
Piotrowska, E: Analysis of morphometric features for computer aided medical diagnosis. PhD thesis, Opole University of Technology (2012)
Stefanowski, J., Wilk, S.: Combining rough sets and rule based classifiers for handling imbalanced data. In: Czaja L. (ed.) Proceedings of Concurrency, Specification and Programming, CS&P 2005 Conference, vol. 2, pp. 497–508 (2005)
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Ewelina, P. (2020). Evaluation of the Classifiers in Multiparameter and Imbalanced Data Sets. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-30604-5_24
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DOI: https://doi.org/10.1007/978-3-030-30604-5_24
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