We then propose a new procedure to resample the data. Our method is based on the idea of eliminating “easy” majority observations before under-sampling them. It has further improved the balanced accuracy of the Random Forest to 83.7%, making it the best approach for the imbalanced data.
Nguyen, S., Niu, G., Quinn, J., Olinsky, A., Ormsbee, J., Smith, R. and Bishop, J. (2019), "Detecting Non-injured Passengers and Drivers in Car Accidents: A New Under-resampling Method for Imbalanced Classification", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 13), Emerald Publishing Limited, pp. 93-105. https://doi.org/10.1108/S1477-407020190000013011Download as .RIS
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