%0 Journal Article
%T 自适应特征选择加权k子凸包分类<br>Weighted k sub-convex-hull classifier based on adaptive feature selection
%A 牟廉明
%J 山东大学学报（工学版）
%D 2018
%R 10.6040/j.issn.1672-3961.0.2017.415
%X 针对问题维数的增加以及不同特征对分类的作用往往不一样,导致k子凸包分类性能降低等问题,设计自适应特征选择加权k子凸包分类方法。根据传统凸包距离存在的不足引入加权k子凸包距离,在测试样本的k邻域内引入距离度量学习技术和正则化技术进行自适应的特征选择,并将自适应特征选择无缝嵌入加权k子凸包优化模型中,这样就能为不同的测试样本在不同的类别中学习自适应特征空间,得到有效的加权k子凸包距离计算方法。试验结果表明,该方法不仅能够进行降维,而且具有明显的分类性能优势。<br>Because of the increase of the dimension of the problem and the effect of different features on classifier, the performance of the k sub-convex-hull classifier was seriously reduced. An adaptive feature selection weighted k sub-convex-hull classifier was designed (AWCH). A weighted k sub-convex-hull classifier was designed according to the shortcomings of conventional convex hull distance. By applying the distance metric learning and regularization technique in the k neighborhood of the test sample, an adaptive feature selection method was designed and seamlessly integrated into the optimization model on the weighted k sub-convex-hull. Through these efforts, for different test samples, an adaptive feature space in different categories could be extracted, and a valid weighted k sub-convex-hull distance could be obtained. Experimental results showed that the AWCH not only reduced the dimension of the problem, but also was significantly superior to similar classifiers.
%K 加权&lt
%K i&gt
%K k&lt
%K /i&gt
%K 子凸包
%K 度量学习
%K 正则化
%K 特征选择
%K 自适应
%K &lt
%K br&gt
%K weighted &lt
%K i&gt
%K k&lt
%K /i&gt
%K sub-convex-hull classifier
%K distance metric learning
%K regularization
%K feature selection
%K adaptive
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.415