A bi-directional sampling based on K-means method for imbalance text classification
Jia Song, Xianglin Huang, Sijun Qin, Qing Song · 2016
This paper studies the imbalanced data classify-cation problem and proposes bi-directional sampling based on clustering (BDSK) for the imbalanced data classification. This algorithm combines SMOTE over-sampling algorithm and under-sampling algorithm based on K-Means to solve the within-class imbalance problem and the between-class imbalance problem. It not only avoid induce too much noise but also resolve the problem of shortage of sample. Experimental results on Tan corpus dataset show that the algorithm can effectively improve the classification performance on imbalanced data sets, especially in the cases when classification performance is heavily affected by class imbalance.