Abstract
In this paper we propose a novel combined approach to solve the imbalanced data issue in the application to the problem of the post-operative life expectancy prediction for the lung cancer patients. This solution makes use of undersampling techniques together with cost-sensitive SVM (Support Vector Machines). First, we eliminate non-informative examples by applying Tomek links together with one-sided selection. Second, we take advantage of using cost-sensitive SVM with penalty costs calculated respecting cardinalities of minority and majority examples. We evaluate the presented solution by comparing the performance of our method with SVM-based approaches that deal with uneven data. The experimental evaluation was performed on real-life data from the postoperative risk management domain.
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Zięba, M., Świątek, J., Lubicz, M. (2014). Cost Sensitive SVM with Non-informative Examples Elimination for Imbalanced Postoperative Risk Management Problem. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J. (eds) Advances in Systems Science. Advances in Intelligent Systems and Computing, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-01857-7_29
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DOI: https://doi.org/10.1007/978-3-319-01857-7_29
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