关键词: 属性选择, 分形维数, 邻接空间, 密度

Abstract: Feature selection has abroad application in machine learning and data mining area，it is always applied as a primary pre-processing step.Selecting feature space which can stand for data set’s fractal characteristics has an important value in revealing the law of data set.Basing on the future of fractal，this paper introduces the density analysis method and points out the defects of existing feature selection method based on fractal dimension.Then a feature selection method based on fractal and changes of neighborhood space density is proposed.In order to evaluate the efficiency of this algorithm，the SVM algorithm and K-fold cross validation are used to evaluate the classification accuracy on three datasets.Experimental results show that this method can achieve a good performance compared with the existing methods，and can identify the better feature space.

Key words: feature selection, fractal dimension, neighborhood space, density