摘要
对于空间中的任一子集,通过基本邻域信息粒子进行逼近,由此提出了邻域信息系统和邻域决策表模型.分析了该模型的性质,并且基于此模型构造了数值型属性的选择算法.利用UCI标准数据集与现有算法进行了比较分析,实验结果表明,该模型可以选择较少的特征而保持或改善分类能力.
To deal with numerical features, a neighborhood rough set model is proposed based on the definitions of δ neighborhood and neighborhood relations in metric spaces. Each object in the universe is assigned with a neighborhood subset, called neighborhood granule. The family of neighborhood granules forms a concept system to approximate an arbitrary subset in the universe with two unions of neighborhood granules： lower approximation and upper approximation. Thereby, the concepts of neighborhood information systems and neighborhood decision tables are introduced. The properties of the model are discussed. Furthermore, the dependency function is used to evaluate the significance of numerical attributes and a forward greedy numerical attribute reduction algorithm is constructed. Experimental results with UCI data sets show that the neighborhood model can select a few attributes but keep, even improve classification power.
出处
《软件学报》
EI
CSCD
北大核心
2008年第3期640-649,共10页
Journal of Software
基金
Supported by the National Natural Science Foundation of China under Grant No.60703013 (国家自然科学基金)
the Development Program for Outstanding Young Teachers in Harbin Institute of Technology of China under Grant HITQNJS.2007.017 (哈尔滨工业大学优秀青年教师培养计划)
the Scientific Research Foundation of Harbin Institute Technology of China under Grant No.HIT2003.35 (哈尔滨工业大学校基金)
关键词
数值特征
粒度计算
邻域关系
粗糙集
可变精度
属性约简
特征选择
numerical feature
granular computing
neighborhood relation
rough set
variable precision
attribute reduction
feature selection
