Research On Incremental Attribute Reduction Algorithm Based On Binary Matrix

Attribute reduction is one of the core contents of rough set theory.The main idea is to delete unnecessary or unimportant attributes in knowledge while keeping the classification ability of information system unchanged.Most of the existing reduction algorithms are designed for the static decision table,but the algorithm research on the dynamic decision table reduction is less.When the samples and attributes of the system change dynamically at the same time,the attribute reduction of the updated system needs to be obtained.The existing static reduction algorithms are inefficient and time consuming in dealing with the dynamic changes,the research of incremental attribute reduction algorithm is needed.The main work of this paper is as follows:(一)Based on the discussion of four commonly used decision table attribute reduction algorithms based on static database,this paper makes a comparative analysis of these four algorithms from three aspects of reduction results,whether they are effective or not,and time complexity,and points out their advantages and disadvantages.(二)In this paper,an improved attribute reduction algorithm of binary discernible matrix is proposed based on the method optimization of the existing binary discernible matrix for sample and attribute dynamic change decision system.When the decision sample changes,the algorithm simplifies the decision table by using Pointers to store objects and obtains simplified equivalence classes,according to the corresponding binary discernibility matrix of new samples are classified and attribute reduction is obtained by attribute frequency function calculation,and the improved algorithm and the algorithm about Jane in reduction results,time and space complexity,etc,the example shows that the improved algorithm is simple,efficient,accurate,practical and complete.Also puts forward a kind of incremental improvement algorithm based on discernibility matrix,the properties change,find nuclear properties through calculation of discernibility matrix,and then to deal with discernibility matrix,determine whether nuclear is the optimal reduction,if not,then you need to the importance degree of attribute by calculation selection in addition to the core attributes of residual properties to join the nuclear attribute importance of big,be reduced as a result,the accuracy of the algorithm is verified by an example,and the reduction result is better and more efficient.(三)In view of the problem that there is a lot of redundant information in the original data of the traffic accident system that the samples and attributes may change dynamically at the same time,the improved binary matrix incremental reduction algorithm is applied to the attribute reduction problem of traffic accident analysis,and the key factors affecting the traffic accident are determined,which provides decision support for the traffic department.