An Imbalanced Classification Method Based on Adaptive Sampling

In view of the problem that traditional resampling methods, which mostly use fixed sampling strategies, cannot change the sampling strategy according to the optimization requirements of the model, this paper proposed an adaptive sampling-based imbalanced classification method (ASIC). This method dynamically adjusts the sampling probabilities of samples of different classes on the training set according to the performance of the classification model on the validation set, so that the sampling probabilities of different classes can be dynamically determined by the requirements of the current classification model. At the same time, this method pays extra attention to the minority classes and gives the minority classes a higher sampling probability under the same other conditions, so as to compensate for the negative impact due to the insufficient number of samples of the minority class and to improve the recognition ability of the model for the minority classes. Experimental results show that this method performs better than the contrasting methods in the geometric mean in terms of both the mean class accuracy and recall. The superiority of the ASIC approach is more obvious when the data distribution is more unbalanced.