摘要
在大数据环境下,序决策信息系统中数据的持续增长导致对象间的优势关系动态变化,高效计算属性约简成为亟待解决的关键问题。为此,提出一种增量单值中智优势条件熵,并由此构建了新的增量式属性约简算法。首先,在单值中智序决策信息系统下给出单值中智优势条件熵;随后,针对4种不同类型的新增对象,深入研究了单值中智优势条件熵的增量更新机制,进而根据该更新机制设计了增量式属性约简算法;最后,选取6个具有优势关系的UCI数据集对增量算法与非增量算法的有效性和高效性进行了对比分析。实验结果表明,新给出的增量属性约简算法在保持相同分类精度的条件下,可以显著提升数据处理的计算效率。
In the big data environment,the continuous growth of data in the ordered decision information system leads to the dynamic change of the dominance relationship between objects.Efficient calculation of attribute reduction has become a key problem to be solved urgently.Therefore,an incremental singlevalued medium-intelligence dominance conditional entropy is proposed,and a new incremental attribute reduction algorithm is constructed accordingly.Firstly,the single-valued medium-intelligence dominance conditional entropy is given under the single-valued medium-intelligence ordered decision information system.Subsequently,for four different types of new objects,the incremental update mechanism of singlevalued medium-intelligence dominance conditional entropy is deeply studied,and then an incremental attribute reduction algorithm is designed according to this update mechanism.Finally,six UCI datasets with dominance relations are selected to conduct a comparative experimental analysis on the effectiveness and efficiency of the incremental algorithm and the non-incremental algorithm.Experimental results show that the newly given incremental attribute reduction algorithm can significantly improve the computational efficiency of data processing while maintaining the same classification accuracy.
作者
骆公志
王聪
LUO Gongzhi;WANG Cong(School of Management,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《数据采集与处理》
北大核心
2025年第5期1207-1221,共15页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(72171124)
江苏高校哲学社会科学研究重大项目(2021SJZDA129)
江苏省教育科学“十四五”规划重点课题(GK-202103)
江苏省研究生科研创新计划项目(KYCX23_0936)。
关键词
优势条件熵
单值中智粗糙集
增量学习
序决策信息系统
属性约简
entropy of dominant condition
single-valued medium-intelligence rough set
incremental learning
order decision information system
attribute reduction
