원문정보
초록
영어
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 st
목차
1. Introduction
2. Cluster Boundary Sampling Method based on Density Clustering
2.1. Density Clustering Algorithm
2.2. Cluster Boundary Under-sampling Method
3. Pruning Algorithm based on Dynamic Threshold KNN
3.1. Complexity and Overlapping Analysis of Imbalanced Data Set
3.2. DT-KNN Pruning Algorithm
4. The Results and Analysis of Experiment
5. Conclusion
Acknowledgements
References