Aimed at problems of feature redundancy caused by the fact that feature correlation was seldom considered in the feature ranking methods,a feature ranking model with redundancy control was proposed.Maximum discrimination ability and minimum redundancy of a feature subset were used as the objective functions of the very model so as to reduce the redundancy among features,and greed and non linear programming methods were employed to solve the model.Experiments were conducted on 9 public datasets and compared with feature ranking,and the result showed that the model can obtain a better classification accuracy and less feature size on most datasets.When non linear programming method is employed,the model can yield a feature subset,on benefit for determining the feature size.This model can be used when correlation exists among features.
