Unsupervised Feature Selection Method for Categorical Features
Shengyi Jiang · Journal of Chinese Computer Systems · 2011
In this paper,a new definition of measuring the importance of features is proposed for categorical data.Furthermore an unsupervised feature selection method based on one-pass clustering algorithm is presented.Theory analysis indicates that the time complexity of the feature selection method is nearly linear with the size and the number of features of dataset.It can be applied in feature selection for high dimensional data.Experimental results on UCI datasets show that the performance obtained by the proposed method is effective and practicable in features selection through comparing with other traditional feature selection approaches.