Feature transformation for efficiently improving performance of HSC

Hyper surface classification (HSC) is a novel classification method based on hyper surface which is put forward by Qing He, etc. Experiments show that HSC can efficiently and accurately classify large-size data in two dimensional space and three-dimensional space. Actually, it is difficult to deal with high dimensional data for HSC. So the dimension reduction (data rearrangement) and ensemble methods (feature subspace) are proposed for HSC. But the method based on ensemble will produce many inconsistent and repetitious data in some density dataset, which influence the classification ability of HSC. To solve the problem, a simple and effective kind of data feature transformation method for enhancing performance of HSC is proposed in this paper. The experimental results show that this method can efficiently reduce the inconsistent and repetitious data, efficiently utilize the data Information, and remarkably improve the classification performance of HSC.