Attribute Reduction Algorithm Based on Discrete Particle Swarm Optimization and Variable Precision Rough Set
Attribute Reduction Algorithm Based on Discrete Particle Swarm Optimization and Variable Precision Rough Set
复制标题
基于离散粒子群优化和变精度粗糙集的属性约简算法
DOI：
10.1142/s0219265921430301
复制
发表时间：
2022-02
影响因子：
0.7
通讯作者：
张雷
中科院分区：
文献类型：
--
作者：
佘志用;宋涛;张雷
证明了属性约简是一个非确定性多项式问题。最小属性约简是粗糙集理论的主要研究内容。研究发现，在传统的粗糙集属性约简算法中，基于离散粒子群优化（DPSO）的粗糙集属性约简算法具有较好的性能。然而，该方法的适应度函数存在一定的局限性。当最小属性约简结果为条件属性集本身时，无法得到正确的结果。为了提高属性约简的精度和效率，提出了一种基于DPSO和变精度粗糙集（VPRS）的属性约简算法。该算法采用VPRS技术对数据进行更精确的处理。构造了新的适应度函数，并将属性依赖作为函数判断依据。它可以随着离散二元粒子群的演化而自动调整，保证了收敛速度和演化方向。实验结果表明，与传统方法相比，该算法具有更强的有效性和更高的应用价值。
Attribute reduction is proved to be a non-deterministic polynomial problem (NP). Minimal attribute reduction is the main research content in rough set theory. We find that in the traditional rough set attribute reduction algorithm, the rough set attribute reduction based on discrete particle swarm optimization (DPSO) performs well. However, the fitness function of this method has some limitations. When the minimum attribute reduction result is the conditional attribute set itself, the correct result cannot be obtained. For enhancing the accuracy and efficiency of attribute reduction, we propose an attribute reduction algorithm based on DPSO and variable precision rough set (VPRS). The proposed algorithm uses VPRS to process data more accurately. The new fitness function is constructed, and the attribute dependence is used as the function judgment basis. It can be adjusted automatically as the discrete binary particle swarm evolves, ensuring the convergence speed and evolution direction. Experimental results show that compared with traditional method, the proposed algorithm has stronger effectiveness and higher application value.