Abstract
Imbalanced datasets constitute a challenge in medical-image processing and machine learning in general. When the available training data is highly imbalanced, the risk for a classifier to find the trivial solution increases dramatically. To control the risk, an estimate on the prior class probabilities is usually required. In some medical datasets, such as breast cancer imaging techniques, estimates on the priors are intractable. Here we propose a solution to the imbalanced support vector classification problem when prior estimations are absent based on a case-dependent transformation on the decision function.
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Acknowledgment
This work has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 656886.
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Illan, I.A., Ramirez, J., Gorriz, J.M., Pinker, K., Meyer-Baese, A. (2019). Case-Based Support Vector Optimization for Medical-Imaging Imbalanced Datasets. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_21
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DOI: https://doi.org/10.1007/978-3-319-94120-2_21
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