Abstract
In the paper a resampling approach for unbalanced datasets classification is proposed. The method suitably combines undersampling and oversampling by means of genetic algorithms according to a set of criteria and determines the optimal unbalance rate. The method has been tested on industrial and literature datasets. The achieved results put into evidence a sensible increase of the rare patterns detection rate and an improvement of the classification performance.
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Vannucci, M., Colla, V. (2018). Genetic Algorithms Based Resampling for the Classification of Unbalanced Datasets. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_3
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DOI: https://doi.org/10.1007/978-3-319-59424-8_3
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