Abstract
In this paper we address a problem arising from the classification of breast cancer malignancy data. Due to the fact that there is much smaller number of patients which are diagnosed with high malignancy, data sets are prone to have a high imbalance between malignancy classes. To overcome this problem we have applied state-of-the-art methods for imbalanced classification to our data set and demonstrate an improvement in the classification sensitivity. The achieved sensitivity for our data set was recorded at 92.34%.
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Krawczyk, B., Jeleń, Ł., Krzyżak, A., Fevens, T. (2014). One-Class Classification Decomposition for Imbalanced Classification of Breast Cancer Malignancy Data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_46
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DOI: https://doi.org/10.1007/978-3-319-07173-2_46
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