Research on Ant Colony Optimization of Oversampling Problem Based on Bootstrap and Maximum Entropy Principle

Classification of imbalanced data is an important research topic in machine learning. In unbalanced data, the minority class has significantly fewer training samples than the majority class, resulting in classification results favoring the majority class. Therefore, we propose a new algorithm for the classification of imbalanced data. The new algorithm is divided into the following three main parts. Firstly, by using the bootstrap method and the maximum entropy principle the new equilibrium data is obtained, which is compared with other oversampling algorithms. Secondly, the obtained equilibrium data are filtered by using the ant colony algorithm. The training set conducive to improving the classifier’s performance is selected. Finally, by using the filtered training set, the classifier is classified and compared with the previously proposed oversampling algorithm. By selecting eight sets of data from the UCI and KEEL databases for classification experiments, the experimental results show that the new algorithm proposed in this study outperforms existing algorithms in various evaluation metrics such as Auc, F
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and Acc.