基于粗糙集理论的多标记数据互补决策约简加速算法  

Rough Set Theory Based Accelerated Algorithm for Complementary Decision Reduct of Multi-label Data

作　　者：李华 王思宇 王雅茹 LI Hua;WANG Siyu;WANG Yaru(Department of Mathematics and Physics,Shijiazhuang Tiedao University,Shijiazhuang,Hebei 050043,China)

机构地区：[1]石家庄铁道大学数理系,河北石家庄050043

出　　处：《南华大学学报（自然科学版）》2022年第3期106-112,共7页Journal of University of South China：Science and Technology

基　　金：国家自然科学基金项目(61806133);国家留学基金项目(201908130072)。

摘　　要：互补决策约简是一种多标记数据属性约简方法,当数据规模较大时,其启发式算法的计算耗时较大。基于粗糙集理论,对互补决策约简启发式算法的加速算法进行了研究。当粒度由粗变细时,在逐步去掉正域的数据集上,首先研究互补决策约简中属性外部重要度的保序性质;基于此,通过逐步缩小数据规模来降低计算约简的耗时,提出了互补决策约简加速算法。加速算法不仅减少了属性约简的计算时间,而且能够保持原始算法的约简结果。Complementary decision reduct is an effective attribute reduction approach for multi-label data.However,the corresponding heuristic algorithm is computationally timeconsuming for large data sets.This paper proposes an accelerated heuristic algorithm of complementary decision reduct based on rough set theory.First,the rank preservation of outer significance measure of attribute in complementary decision reduct is studied on a dataset of positive region that is gradually removed when the granulation changes from coarse to fine.Then,an accelerated heuristic algorithm is proposed which can decrease the time-consuming by gradually shrinking the data scale.The accelerated algorithm not only speeds up the process of attribute reduction,but also preserves the reduction results of the original algorithm.

关 键 词：多标记数据 互补决策约简 粗糙集 保序性 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]