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.