Adaptive Sampling Algorithm Based on Border Information
In order to address the issues of limited synthetic region,potential generalization of minority class to majority class,and introduction of noise in the synthetic minority over-sampling technique (SMOTE) algorithm,a oversampling method based on noise-filtering and boundary-point adaptive sam-pling was proposed.Firstly,the K-nearest neighbors algorithm was utilized for noise filtering.Next,the boundary points were identified and appropriate points among them were selected as root samples,with the candidate samples being chosen as the farthest points in the K-nearest neighbors of the same class with the root samples based on the Euclidean distance.Subsequently,the number of synthetic samples to be generated for each root sample was determined based on the boundary information carried by the root sam-ples.An N-dimensional sphere was created using the root samples and the candidate samples as the syn-thesis interval for the samples.Finally,the synthesized samples were assessed to ensure their compliance with the conditions.Experimental results demonstrated that the proposed method yielded samples with higher quality compared to SMOTE and its common variants.
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维普
