摘要
正向近似是刻画目标概念组成结构的一种有效方法.文中针对非完备决策表现有特征选择算法计算耗时过大的缺陷,提出了一种基于正向近似的通用特征选择加速算法.该算法不仅对候选属性具有保序性,而且通过在特征选择过程中减少样本数据的规模来降低计算耗时,加速特征选择过程.实验结果进一步验证了加速算法的有效性和高效性.特别指出的是,随着属性的增多和数据量的增大,加速算法的性能通常会更好,可有效应用于海量数据的特征选择.
Positive approximation is an effective approach to characterizing the structure of a target concept in information systems. To overcome the limitation of time-consuming of all existing feature selection algorithms in incomplete decision tables. This paper provides a general accelerated algorithm based on the positive approximation. This modified algorithm both possesses the rank preservation of attributes and reduces the time consumption through reducing the scale of data, which effectively accelerates the process of feature selection in incomplete decision tables. Experimental analyses verify the validity and efficiency of the accelerated algorithm. It is deserved to point out that the performance of these modified algorithms are getting better in time reduction with the data set becoming larger.
出处
《计算机学报》
EI
CSCD
北大核心
2011年第3期435-442,共8页
Chinese Journal of Computers
基金
国家自然科学基金(71031006
60903110
60773133
70971080)
国家"九七三"重大基础研究发展规划项目基金(2007CB311002)
山西省自然科学基金(2008011038
2009021017-1)资助~~
关键词
特征选择
非完备决策表
粗糙集
正向近似
feature selection
incomplete decision tables
rough sets
positive approximation
