Unbalanced data classification algorithm based on improved SMOTE
The SMOTE algorithm is a classic oversampling algorithm for handling imbalanced data,and this article improves it.Firstly,the k-means algorithm is used to cluster the original dataset.Use the class discriminant function to filter the clustering samples and select"safe samples".Finally,a new oversampling rate is used to linearly interpolate the"safe samples",and the LMKNN method is used during the interpolation process.This algorithm was applied to imbalanced datasets separately with SMOTE and KNSMOTE,and the classification performance was compared using SVM classification algorithm.The results show that the algorithm used in this paper has better classification performance in imbalanced datasets such as Abalone and Ecoli,verifying the effectiveness of the algorithm.
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