一种序决策信息系统中的快速属性约简算法

A fast attribute reduction algorithm for sequential decision information system

为了提高无核或少核序决策信息系统中现有属性约简算法的执行效率,本文以知识粒度表征的属性重要度为启发信息并结合前向属性约简方法提出了一种新的属性约简算法。首先,介绍优势粗糙集方法的相关基础知识,并将经典粗糙集中基于知识粒度的属性约简算法引入优势粗糙集方法中,得到可处理序决策信息系统的属性约简算法;然后,通过分析序决策信息系统中知识粒在属性数目变化条件下的粗化与细化过程,得出相对冗余属性的判断定理,由此结合前向属性约简方法设计了快速属性约简算法;最后分析比较了2种算法的时间复杂度并选取了6个不同的UCI数据集进行算法性能的测试,测试结果表明,本文提出的算法比现有的属性约简算法高效。

Based on the attribute importance of knowledge granularity representation and the forward attribute reduction method,a new attribute reduction algorithm is proposed in order to improve the efficiency of existing attribute reduction algorithms in non-kernel or less kernel order decision information systems.Firstly,the basic knowledge of the dominant rough set method is presented and the attribute reduction algorithm based on knowledge granularity of classical rough set is introduced into the dominant rough set method,thus the attribute reduction algorithm of processable decision information system is obtained.Then,by analyzing the coarsening and thinning process of knowledge granules in the order decision information system under the condition of variable attribute number,the judgment theorem of relative redundant attribute is obtained,and the fast attribute reduction algorithm is designed by combining forward attribute reduction method.Finally,the time complexity of the two algorithms is analyzed and compared,and 6 different UCI data sets are selected to test the algorithm performance.The test results show that the proposed algorithm is more efficient than the existing attribute reduction algorithms.