Abstract: Feature selection can effectively remove redundant and irrelevant features from high-dimensional data and retain important features,thus reducing the complexity of model computation and improving model accuracy.While in feature selection process,to deal with these noisy data that may affect the classification effect,such as outlier points and boundary points,a feature selection method based on rough set and density peak clustering is proposed.At first,noisy data are removed by density peak clustering method and cluster class centers are picked out.Then,the data are divided by cluster class centers by combining the idea of rough set theory,and the feature importance evaluation measure is defined according to the assumption that the data points of same cluster have same label.Finally,a heuristic feature selection algorithm is designed to pick up the feature subset that can makes for a purer homogeneous cluster structure.Experimental comparisons of classification accuracy,number of selected features and running time are conducted with other algorithms on six UCI datasets,and the experimental results verify the effectiveness and efficiency of the proposed algorithm.

Key words: Feature selection, High-dimensional data, Noisy data, Rough sets, Density peak clustering