Imbalanced Data SVM Classification Method Based on Cluster Boundary Sampling and DT-KNN Pruning

Abstract

This paper presents a SVM classification method based on cluster boundary sampling and sample pruning. We actively explore an effective solution to solve the difficult problem of imbalanced data set classification from data re-sampling and algorithm improving. Firstly, we creatively propose the method of cluster boundary sampling, using the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of re-sampling more scientifically and accurately. Secondly, we put forward a new sample pruning algorithm based on dynamic threshold KNN to deal with the complexity and overlapping problem of imbalanced data set. The phenomenon of data complexity and overlapping will reduce the classification performance and generalization ability of SVM classifier. Experiments show that our method acquires obviously promotion effect in various different imbalanced data sets and it can prove the validity and stability.