基于改进二元萤火虫群优化算法和邻域粗糙集的属性约简方法

Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set

针对数据降维和去冗问题,提出基于改进的二元萤火虫群优化算法和邻域粗糙集的属性约简方法.首先,运用反向学习协同初始化种群,并基于Sigmoid变化函数的映射进行二进制编码,引入Levy飞行位置更新策略,提出改进二元萤火虫群优化算法.再以邻域粗糙集作为评价准则,以改进算法作为搜索策略,进行属性约简.最后,通过在标准UCI数据集上的实验验证属性约简方法的有效性,并验证文中算法具有较优的收敛速度和精度.

Aiming at the problems of dimension reduction and redundancy removing,an attribute reduction method based on improved binary glowworm swarm optimization algorithm and neighborhood rough set is proposed.Firstly,the population is collaborative initialization using reverse learning,and the mapping of the change function based on Sigmoid is employed for binary coding,and an improved binary glowworm opti-mization algorithm is proposed with Levy flight position update strategy.Secondly,neighborhood rough set is employed as an evaluation criterion,and the proposed algorithm is utilized as an search strategy for attribute reduction.Finally,experiments on the standard UCI datasets demonstrate the effectiveness of the attribute reduction method,and the better convergence speed and accuracy of the proposed algorithm is verified.