Spectral clustering based oversampling:oversampling taking within class imbalance into consideration
Luo Zicha · Computer Engineering and Applications Journal · 2014
Imbalanced datasets are one of the most crucial challenges encountered by data mining techniques. Oversampling has been proven to be a very effective method in dealing with imbalanced datasets. However, traditional oversampling methods pay no attention to within class imbalance which is pervasive in real world datasets. To resolve this problem, this paper proposes an oversampling method based on modified spectral clustering. This method first automatically decides the best number of clusters. Then modified spectral clustering is applied to minority samples. Based on the number of samples contained in each cluster, this proposal judges the number of samples which shall be generated inside each cluster to get a dataset which is balanced both between and within class. This method is tested in 4 real world datasets and one simulated dataset. It is proven to be effective. Moreover, a comparison between traditional k-means clustering based oversampling and the method proposed in this paper is conducted. The results are analyzed and explained.