Differential Evolution Feature Selection by Using Neighborhood Rough Set Model
When this paper applies a binary differential evolution algorithm with Hybrid Encoding to the feature selection,neighborhood rough set model is introduced to make full use of neighborhood local information and also can avoid the discrete process of numerical attributes.In order to evaluate the feature set better,weighted neighborhood recognition rate is proposed to overcome the possible problems of dependency measure and neighborhood recognition rate measure.The experimental results with UCI data set show that this method is effective and efficient.