Imbalanced Data Classification Method Based on Clustering and Voting Mechanism

In order to resolve the poor predictive accuracy problems over minority class, which caused by imbalanced distribution existed in imbalanced data classification, this paper proposes an Imbalanced Data Classification Method Based on Clustering and Voting Mechanism. This method gets various clustered results through clustering, then acquires the final data clusters via voting mechanism, besides, determines sampling ratio from the feature of final data clusters and data inclination. It can guarantee the minority class quantity when compressing data sets. The experimental results show that our method is effective and feasible, besides has high classification accuracy.