区间值决策表的正域增量式属性约简算法  

Incremental attribute reduction algorithm of positive region in interval-valued decision tables

作　　者：鲍迪 张楠[1,2] 童向荣 岳晓冬[3] BAO Di;ZHANG Nan;TONG Xiangrong;YUE Xiaodong(Key Lab for Data Science and Intelligence Technology of Shandong Higher Education Institutes(Yantai University),Yantai Shandong 264005,China;School of Computer and Control Engineering,Yantai University,Yantai Shandong 264005,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China)

机构地区：[1]数据科学与智能技术山东省高校重点实验室(烟台大学),山东烟台264005 [2]烟台大学计算机与控制工程学院,山东烟台264005 [3]上海大学计算机工程与科学学院,上海200444

出　　处：《计算机应用》2019年第8期2288-2296,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61403329,61572418,61702439,61572419,61502410);山东省自然科学基金资助项目(ZR2016FM42,ZR2018BA004)~~

摘　　要：实际应用中存在大量动态增加的区间型数据,若采用传统的非增量正域属性约简方法进行约简,则需要对更新后的区间值数据集的正域约简进行重新计算,导致属性约简的计算效率大大降低。针对上述问题,提出区间值决策表的正域增量属性约简方法。首先,给出区间值决策表正域约简的相关概念;然后,讨论并证明单增量和组增量的正域更新机制,提出区间值决策表的正域单增量和组增量属性约简算法;最后,通过8组UCI数据集进行实验。当8组数据集的数据量由60%增加至100%时,传统非增量属性约简算法在8组数据集中的约简耗时分别为36.59s、72.35s、69.83s、154.29s、80.66s、1498.11s、4124.14s和809.65s,单增量属性约简算法的约简耗时分别为19.05s、46.54s、26.98s、26.12s、34.02s、1270.87s、1598.78s和408.65s,组增量属性约简算法的约简耗时分别为6.39s、15.66s、3.44s、15.06s、8.02s、167.12s、180.88s和61.04s。实验结果表明,提出的区间值决策表的正域增量式属性约简算法具有高效性。There are a large number of dynamically-increasing interval data in practical applications.If the classic non-incremental attribute reduction of positive region is used for reduction,it is necessary to recalculate the positive region reduction of the updated interval-valued datasets,which greatly reduces the computational efficiency of attribute reduction.In order to solve the problem,incremental attribute reduction methods of positive region in interval-valued decision tables were proposed.Firstly,the related concepts of positive region reduction in interval-valued decision tables were defined.Then,the single and group incremental mechanisms of positive region were discussed and proved,and the single and group incremental attribute reduction algorithms of positive region in interval-valued decision tables were proposed.Finally,8 UCI datasets were used to carry out experiments.When the incremental size of 8 datasets increases from 60% to 100%,the reduction time of classic non-incremental attribute reduction algorithm in the 8 datasets is 36.59 s,72.35 s,69.83 s,154.29 s,80.66 s,1 498.11 s,4 124.14 s and 809.65 s,the reduction time of single incremental attribute reduction algorithm is 19.05 s,46.54 s,26.98 s,26.12 s,34.02 s,1 270.87 s,1 598.78 s and 408.65 s,the reduction time of group incremental attribute reduction algorithm is 6.39 s,15.66 s,3.44 s,15.06 s,8.02 s,167.12 s,180.88 s and 61.04 s.Experimental results show that the proposed incremental attribute reduction algorithm of positive region in interval-valued decision tables is efficient.

关 键 词：粗糙集 区间值决策表 相容关系 正域 增量式属性约简 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]