Multiple adaptive over-sampling for imbalanced data evidential classification

Over-sampling approaches focus on generating samples to balance the dataset and have been widely applied in classifying imbalanced data. However, existing approaches do not take into account the uncertainty of generated samples, which may alter the data distribution and introduce uncertain information into the classification process. To tackle this issue, we propose a multiple adaptive over-sampling approach (MAO) for classifying imbalanced data based on evidence reasoning. First, we construct balanced training sets through multiple adaptive over-sampling for the minority class, which characterizes the uncertainty of over-sampling. Then, we define the intra- and inter-class inconsistency of data distribution after over-sampling to quantify the weights of different classifiers trained by various balanced subsets, weakening the negative impact of changes in data distribution on classification. Finally, we employ neighbor information to revise the results of samples that are hard to classify correctly, to avoid the risk of misclassification caused by uncertain synthetic samples to some extent. The effectiveness of MAO has been verified on several real imbalanced datasets by comparing it with other related approaches.