An ensemble learning algorithm with Gaussian-based oversampling

The class imbalance learning widely occurs in classification tasks in the research field of data mining, such as manufacturing quality conditions, medical diagnosis, financial service, etc. The synthetic minority over-sampling technique (SMOTE) is a common technique to deal with imbalanced datasets, which can be enhanced using the framework of the boosting algorithm. However, this strategy can easily result in the lack of diversity of the base classifiers in the ensemble learning system. On this account, a boosting learning algorithm integrated Gaussian process smote oversampling is proposed to solve the imbalance learning problem, namely Gaussian-based smote in boosting (GSMOTEBoost). In order to improve the robustness of the classification system, the proposed GSMOTEBoost algorithm is developed using the framework of AdaBoost, in which a smote oversampling technology based on Gaussian process is used to increase the diversity of the base classifiers for each iteration. To verify the effectiveness of our algorithm, we develop the experiments on twenty datasets selected from the KEEL repository with these well-known imbalance learning algorithms. The G-mean, F-measure and AUC are considered as the assessment metrics and the hypothesis testing is used to analyze the experimental results. The obtained results, supported by the proper statistical analysis, indicate that the proposed GSMOTEBoost significantly outperforms the comparison methods. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.