Intuitionistic Fuzzy Entropy Feature Selection Algorithm Based on Adaptive Neighborhood Space Rough Set Model

Feature selection is a very vital technology in data preprocessing.In this method, some most effective features are mainly selected from the features of original data sets, which is aimed to reduce the dimension of data sets.Accordingly, the performance of the learning algorithm can be improved.In the feature selection algorithms based on the neighborhood rough set model, without considering data uneven distribution, there currently exist some defects in the neighborhood of object.To solve this problem of data uneven distribution, variance is adapted to measure the distribution of the data, and the binary neighborhood space is redefined, then the rough set model of the adaptive binary neighborhood space is proposed according to this binary neighborhood space.As well as, the new rough set model of the adaptive binary neighborhood space is combined with the neighborhood intuitionistic fuzzy entropy as the method of the evaluation of features, and then the corresponding feature selection algorithm is also constructed.The experimental results of UCI show that the proposed intuitionistic fuzzy entropy feature selection algorithm can select smaller feature subsets which have higher accuracy of classification, at the same time, the intuitionistic fuzzy entropy feature selection algorithm based on adaptive neighborhood space rough set model also has less time consumption.Therefore, the proposed feature selection algorithm has stronger superiority in this paper.