区间值决策系统的特定类β分布约简

Class-specific β distribution reduction in interval-valued decision systems

属性约简是粗糙集理论的重要研究方向之一,区间值决策系统的β分布约简保持约简前后对应的β分布不变。在实际需求中,属性约简通常只需要关注某一决策类而非所有的决策类,本文在区间值决策系统中的β分布约简基础上提出了基于特定类的β分布约简理论框架。首先,定义了特定类的β分布约简基本概念,然后构造了特定类的β分布约简差别矩阵,最后提出基于差别矩阵的特定类β分布约简算法。在实验中,采用6组UCI数据集分别在全类算法和特定类算法进行约简结果和约简效率的比较。结果表明,本算法约简结果能保持关于特定类对应的β分布约简前后不变,特定类算法的约简长度小于等于全类算法的约简长度,且算法效率高于全类算法效率。

Attribute reduction is one of the important research points in rough set theory.The goal ofβdistribution reduction in interval-valued decision systems is to keep the correspondingβdistribution of objects unchanged.In actual needs,attribute reduction usually only needs to focus on specific decision class rather than all decision classes.This paper proposes a theoretical of class-specificβdistribution reduction in interval-valued decision systems.First of all,the basic concept of class-specificβdistribution reduction is defined,and then the discernibility matrix corresponding to the class-specificβdistribution is constructed.Finally,a class-specificβdistribution reduction algorithm based on the discernibility matrices is proposed.In experiments,six UCI data sets are used to compare reduction results and reduction efficiency of BRADM algorithm and CSBRADM algorithm.The experiments results show that the reduction results of class-specific algorithm can keep theβdistribution for class-specific unchanged,and reduction length of algorithm for specific class is less than or equal to reduction length of algorithm for all classes,and the CSBRADM algorithm efficiency is higher than the BRADM algorithm.