Abstract
In supervised classification, the class imbalance problem causes a bias that results in poor classification for the minority class. To face this problem, particularly in supervised classification with mixed data, some oversampling methods for mixed data have been reported in the literature. However, there is no experimental study comparing and evaluating these methods in a common setting. Therefore, in this paper, we present an experimental comparison of state-of-the-art oversampling methods designed specifically for mixed datasets. Our study reports the best oversampling methods for mixed data in terms of oversampling quality, taking into account the imbalance ratio, and runtime.
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The corresponding author thanks the National Council of Science and Technology of Mexico for partly support this work through a scholarship grant.
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Rodríguez-Torres, F., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2021). Experimental Comparison of Oversampling Methods for Mixed Datasets. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_8
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