Department of Computer Scienne, Huizhou University, Huizhou 516007, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China 在期刊界中查找 在百度中查找 在本站中查找
Dimension reduction is important in machine learning. The two methods of dimension reduction are feature extraction and feature selection. Scatter degree is one of the feature selection methods which attribute a degree of scattering for each feature. Features are selected that have higher scatter degree. In this paper, classification error has been reduced by considering other aspects in computing scatter degree. Experiments on UCI dataset show that improved scatter degree have a good performance on feature selection.