Immune clonal multi-objective algorithm for unsupervised feature selection

被引：0
|
作者：
Shang R.-H. [1 ]
Jiao L.-C. [1 ]
Wu J.-S. [1 ]
Ma W.-P. [1 ]
Li Y.-Y. [1 ]
机构：
[1] Ministry of Education Key Lab. of Intelligent Perception and Image Understanding, Xidian Univ.
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2010年 / 37卷 / 01期
关键词：
Clonal selection; Multi-objective optimization; Unsupervised feature selection;
D O I：
10.3969/j.issn.1001-2400.2010.01.004
中图分类号：
学科分类号：
摘要：
The unsupervised feature selection is transferred into a multiobjective optimization problem, and the immune clonal selection algorithm for multi-objective optimization is applied to solve it. Firstly, the unsupervised feature selection problem is translated into multi-objective problem. Secondly, the model and the objective functions are constructed. Lastly, each feature of significance is optimized by increasing the significance of the related features and decreasing the significance of the unrelated features. Experimental results on UCI data sets show that the error recognition rate is decreased and that the effectiveness and potential of the method are validated.
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页码：18 / 22
页数：4
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