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兴发pt登录学报, 2019, 36(5): 93-96     https://doi.org/10.16039/j.cnki.cn22-1249.2019.05.022
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基于局部类相似的特征选择方法
钱有程
兴发pt登录 理学院
Local Class Similarity Based Feature Selection Method
QIAN Youcheng
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摘要 

常用的特征选择方法利用样本空间的整个区域提取最优的特征子集。与此相反,本文中提出一种新的局部特征选择方法,即样本空间的每个区域都与各自不同的最优特征集相关联,这些特征集能够最优地适应样本空间的局部变化。同时,在求解最优特征集对应的子空间时,基于最近邻思想,本文提出了一种度量测试数据与各个类相似性的方法,用来对测试样本进行分类。本文提出的方法可以描述为线性规划优化问题,因此可以通过简单的凸优化来求解全局最优解。在三组真实数据集和三个主流的方法上进行的对比实验结果证明了该算法的可行性和有效性。

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钱有程
关键词:  局部特征选择  分类  最近邻搜索  线性规划     
Abstract: 

The common feature selection methods utilize the entire region of the sample space to extract an optimal subset of features. In contrast, this paper proposes a new local feature selection method, in which each region of the sample space is associated with a different optimal feature set, which can optimally adapt to the local variation of the sample space. At the same time, when solving the subspace corresponding to the optimal feature set, based on the concept of the nearest neighbor, this paper proposes a method to measure the similarity between the test data and each class to classify the test samples. The method proposed in this paper can be expressed as a linear programming optimization problem, so the global optimal solution can be solved by simple convex optimization. The experimental results on three real datasets and three mainstream methods demonstrate the feasibility and effectiveness of the proposed algorithm.

Key words:  local feature selection    classification    nearest neighbor searching    linear programming
               出版日期:  2019-05-25      发布日期:  2019-05-25      整期出版日期:  2019-05-25
ZTFLH:  O212.1  
  TP18  
引用本文:    
钱有程. 基于局部类相似的特征选择方法 [J]. 兴发pt登录学报, 2019, 36(5): 93-96.
QIAN Youcheng. Local Class Similarity Based Feature Selection Method . Journal of Jilin Institute of Chemical Technology, 2019, 36(5): 93-96.
链接本文:  
http://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2019.05.022  或          http://xuebao.jlict.edu.cn/CN/Y2019/V36/I5/93
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