摘要
数值型不完备信息系统的特征选择方法大多是以容差关系为基础,但是这种处理方式存在数据相似性刻画过于宽松的缺陷.文中提出邻域量化容差关系的粗糙集模型,在该模型的基础上定义邻域量化容差条件熵,分析相关性质,根据邻域量化容差条件熵的单调性构造相应的特征选择算法.实验表明,文中算法在特征选择结果、运行时间和分类精度方面具有优越性.
The existing methods of feature selection are mostly based on tolerance relation in the numerical incomplete information system. However, the data similarity characterization is too loose in these approaches. Therefore, the rough set model of neighborhood valued tolerance relation is proposed in this paper. The neighborhood valued tolerance condition entropy is defined on the basis of the model. And the related properties are analyzed. Finally, the corresponding algorithm is constructed according to the monotonicity of neighborhood valued tolerance condition entropy. Experimental results show that the proposed algorithm is superior to the existing algorithms in terms of the feature selection results, arithmetic operation time and classification accuracy.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2017年第5期416-428,共13页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61602004
61300057)
安徽省自然科学基金项目(No.1508085MF127)
安徽省高等学校自然科学研究重点项目(No.KJ2016A041)
安徽大学信息保障技术协同创新中心公开招标课题(No.ADXXBZ2014-5
ADXXBZ2014-6)
安徽大学博士科研启动基金项目(No.J10113190072)资助~~
关键词
特征选择
不完备信息系统
量化容差关系
邻域
条件熵
Feature Selection, Incomplete Information System, Valued Tolerance Relation, Neighborhood, Condition Entropy
