Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (11): 95-102.DOI: 10.3778/j.issn.1002-8331.2005-0101

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Hybrid Filter and Improved Adaptive GA for Feature Selection

QIU Yunfei, GAO Huacong   

  1. School of Software, Liaoning Technical University, Huludao, Liaoning 125100, China
  • Online:2021-06-01 Published:2021-05-31



  1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125100


Aiming at the problem of dimension disaster and over fitting in feature selection of high dimension small sample data, this paper proposes a feature selection method(ReFS-AGA) based on mixed Filter mode and Wrapper mode. Firstly, the ReliefF algorithm and normalized mutual information are combined to evaluate the correlation of features and quickly select important features. Then, an improved adaptive genetic algorithm is used to balance the diversity of features. At the same time, the objective is to minimize the number of features and maximize the classification accuracy, and the number of features is selected as the adjusting item to design a new evaluation function, which efficiently obtains the optimal feature subset in the iterative evolution process. In this paper, different classification algorithms are used to classify and recognize the simplified feature subset on the gene expression data. The experimental result shows that this method effectively eliminates the irrelevant features and improves the efficiency of feature selection. Compared with the ReliefF algorithm and the two-stage feature selection algorithm mRMR-GA, the average classification accuracy is improved by 11.18 percentage points and 4.04 percentage points respectively when the minimum feature subset dimension is obtained.

Key words: feature selection, Filter mode, ReliefF algorithm, normalized mutual information, adaptive genetic algorithm



关键词: 特征选择, Filter模式, ReliefF算法, 归一化互信息, 自适应遗传算法