基于不完备混合序信息系统的增量式属性约简OACSTPCD

Because the data in the big data environment presents the characteristics of dynamic updating,incremental attribute reduction is attract-ing increasing research attention in the field of rough set theory.As a common information system,the incomplete hybrid ordered information systems(IHOIS)is still without the study of the incremental attribute reduction.To address this issue,an incremental attribute reduction al-gorithm for object updates is proposed for IHOIS in this paper.Firstly,a neighborhood tolerance dominance relation is proposed to build a new neighborhood dominance rough set based on binary relation.In succession,a neighborhood dominance conditional entropy is defined and further serves as a heuristic function to design a non-incremental attribute reduction algorithm for IHOIS.Then,the neighborhood tolerance dominance relation and neighborhood dominance conditional entropy were reconstructed in the form of matrices.In response to the dynamic updates of the IHOIS,matrix-based calculation strategies are applied to study the incremental updates of neighborhood dominance conditional entropy with both the increasing and decreasing of the information system objects.Finally,the update mechanism of neighborhood dominance conditional entropy is utilized to develop the incremental update algorithms for attribute reduction for both the increasing and decreasing of the IHOIS objects.The ex-perimental results show that compared with the non-incremental algorithm,the incremental algorithm reduces the number of attributes by 3.6%on average,improves the classification accuracy by 2.4%on average,and improves the efficiency of attribute reduction by about 10 times on aver-age.Compared with other incremental algorithms,the proposed incremental algorithm reduces the number of attributes by 9.0%on average,im-proves the classification accuracy by 2.1%on average,and increases the average efficiency of attribute reduction by 94%.Based on the reported results,it can be concluded that the proposed incremental algorithms have higher performance in both performance and efficiency of the attribute reduction task.