A feature selection method based on adaptive simulated annealing genetic algorithm

Hao Zhang*, Ran Tao, Zhi Yong Li, Hua Du

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

14 引用 (Scopus)

摘要

Feature selection is one of important problems in machine learning and pattern recognition areas. For high demensian data, feature dimension can be decreased under the condition of ensuring data integrity and classification accuracy can be improved by feature selection. A feature selection method based on adaptive simulated annealing genetic algorithm was proposed, which embeds the simulated annealing algorithm in the circle of adaptive genetic algorithm and uses the feature that simulated annealing algorithm has the strong ability of local search and makes searching process avoid sinking into the local optimal solution, to solve the shortcomings of slow convergence speed and high time complexity. The experiment results show that the method can guarantee the correct rate of classification and improve the efficiency of feature selection.

源语言英语
页(从-至)81-85
页数5
期刊Binggong Xuebao/Acta Armamentarii
30
1
出版状态已出版 - 1月 2009

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Zhang, H., Tao, R., Li, Z. Y., & Du, H. (2009). A feature selection method based on adaptive simulated annealing genetic algorithm. Binggong Xuebao/Acta Armamentarii, 30(1), 81-85.