一种基于改进差别矩阵的属性约简增量式更新算法

粗糙集方法提供了一种新的处理不精确、不完全与不相容知识的数学工具.属性约简是粗糙集理论的重要研究内容之一,已有的大多数属性约简算法主要考虑信息系统(或决策表)不变的情况,有关属性约简的增量式更新算法却报道不多.为此,文中提出一种基于改进差别矩阵的属性约简增量式更新算法,主要考虑对象动态增加情况下属性约简的更新问题.该算法可通过快速更新差别矩阵,在动态求解核的基础上,利用原有的属性约简有效地进行属性约简的增量式更新,因而可提高属性约简的更新效率.理论分析表明,该文提出的算法是有效可行的.

An Incremental Updating Algorithm for Attribute Reduction Based on Improved Discernibility Matrix

Rough set theory is a new mathematical tool to deal with imprecise,incomplete and inconsistent data.Attribute reduction is one of important parts researched in rough set theory.Many existing algorithms mainly aim at the case of stationary information system or decision table,very little work has been done in updating of an attribute reduction.Therefore,in this paper,the authors introduce an incremental updating algorithm for attribute reduction based on discernibility matrix in the case of inserting,which only inserts a new row and column,or deletes one row and updates corresponding column when updating the decernibility matrix.After dynamically computing a core,attribute reduction can be effectively updated by utilizing the old attribute reduction.Theoretical analysis shows that the algorithm of this paper is efficient and feasible.