Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (20): 164-171.DOI: 10.3778/j.issn.1002-8331.2012-0021

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WKNN Feature Selection Method Based on Self-Tuning Adaptive Genetic Algorithm

CHEN Qianru, LI Yali, XU Kequan, LIU Yilong, WANG Shuqin   

  1. College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
  • Online:2021-10-15 Published:2021-10-21



  1. 天津师范大学 计算机与信息工程学院,天津 300387


In view of the fact that most of the existing feature selection methods based on [K]-nearest neighbor and genetic algorithm do not take into account the different importance of each feature and are prone to premature convergence, especially the local optimal solution problem, a weighted [K]-nearest neighbor feature selection method based on self-tuning adaptive genetic algorithm is proposed in this paper. The method uses the weighted [K]-nearest neighbor algorithm to predict the category of samples and assigns a weight for each feature to measure the feature’s classification ability, then it uses self-tuning adaptive genetic algorithm to adjust the mutation rate, population size and convergence threshold, and searches for the optimal feature weight vector in the process of iterative evolution. In order to evaluate the effectiveness of this method, it is compared with the existing seven feature selection methods on five standard data sets. Experimental results show that this method is effective and has high classification performances.

Key words: feature selection, weighted [K]-nearest neighbor, self-tuning adaptive genetic algorithm, parameter tuning, real coding



关键词: 特征选择, 加权[K]近邻, 自调优自适应遗传算法, 参数调优, 实数编码