Imbalanced Data Classification Method Based on LSSASMOTE

Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper. First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed. Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate. In addition, the oversampled samples were noise processed by Tomek Link. In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data. Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually. The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.