【摘要】 k子凸包分类方法在实际问题中有广泛应用。但随着问题维数的增加,该方法计算得到的凸包距离非常接近甚至相等,这严重影响了分类性能。针对此问题,本文设计了一种基于特征选择的相对k子凸包分类方法。首先根据绝对凸包距离存在的不足引入相对k子凸包距离,然后在k邻域内利用判别正则化技术进行特征选择,并将特征选择融入相对k子凸包优化模型中,为每个测试样本在不同的类别中学习一个自适应的特征子集,从而得到一个用于分类的有效相对k子凸包距离。实验结果表明,该方法不仅能够进行特征选择,而且分类性能也有了明显提高。更多还原

【Abstract】 The k sub-convex-hull classifier is widely used in practical problems.But with the increase of the dimension of the problem,these convex distances calculated by the method are very close to or even equal,which seriously affectes the performance of classification.To resolve the above problems,a relative k sub-convex-hull classifier based on feature selection(FRCH)is designed in this paper.Firstly,the definition of the relative k sub-convex-hull is introduced according to the shortcomings of absolutely convex hull distance.Then,the feature selection is carried out by using the discriminant regularization technique in the k neighborhood.Moreover,the feature selection method is embedded in the optimization model on the relative kconvex hull.Through these efforts,an adaptive feature subset in different categories for each test sample can be extracted,and a valid relative ksub-convex-hull distance can be obtained.Experimental results show that our FRCH not only can make the feature selection practicable,but also significantly improves the classification performance of the k sub-convex-hull classifier.更多还原