数据采集与处理2017，Vol.32Issue(5)：1005-1011,7.DOI:10.16337/j.1004-9037.2017.05.018
基于特征选择的相对k子凸包分类方法
Relative k Sub-Convex-Hull Classifier Based on Feature Selection
摘要
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 k convex hull.Through these efforts,an adaptive feature subset in different categories for each test sample can be extracted,and a valid relative k sub-convex-hull distance can be obtained.Experimental results show that our FRCH not only can make the feature selection practicable,but alsosignificantly improves the classification performance of the k sub-convex-hull classifier.关键词
相对k子凸包分类/自适应/判别正则化/特征选择Key words
relative k sub-convex-hull classifier/adaptive/discriminant regularization/feature selection分类
信息技术与安全科学引用本文复制引用
牟廉明,刘好斌..基于特征选择的相对k子凸包分类方法[J].数据采集与处理,2017,32(5):1005-1011,7.基金项目
国家自然科学基金(10872085)资助项目 （10872085）
四川省科技厅科技计划重点项目基金(2017JY0199)资助项目 （2017JY0199）
内江师范学院自然科学重点项目基金(12NJZ03)资助项目. （12NJZ03）
