融合粗糙集和扩散二元萤火虫算法的属性约简方法  

Attribute reduction method combined with spread binary glowworm swarm optimization and rough set

作　　者：程美英[1,2,3] 倪志伟[1,2] 朱旭辉[1,2] 

机构地区：[1]合肥工业大学管理学院,安徽合肥230009 [2]过程优化与智能决策教育部重点实验室,安徽合肥230009 [3]新加坡南洋理工大学计算机科学与工程学院计算智能中心实验室,新加坡639798

出　　处：《系统工程与电子技术》2016年第10期2449-2456,共8页Systems Engineering and Electronics

摘　　要：从一维细胞自动机模型入手,将自然界中种群的扩散行为引入二元萤火虫算法(binary glowworm swarm optimization,BGSO)中,提出了一种扩散二元萤火虫算法(spread binary glowworm swarm optimization,SBGSO)。该算法对萤火虫个体设置营养值及营养阈值的上下限,然后执行扩散操作,以正态分布方式产生新的个体,并淘汰一些持续表现很差的个体,释放资源给其他个体,以保持种群的动态多样性。然后将SBGSO作为搜索策略,粗糙集(rough set,RS)作为评价准则,应用于大数据预处理的属性约简问题。为验证本文算法的可行性,采用5个UCI数据集进行实验,并结合10-fold和支持向量机(support vector machine,SVM)算法对预测结果分类准确率进行分析,通过与其他算法对比,表明本文算法具有较好的约简效果。Starting from the one-dimensional cellular automata model, the spread mechanism is introduced to binary glowworm swarm optimization （BGSO）, and a spread binary glowworm swarm optimization （SBGSO） is proposed. In SBGSO, nutrition value and nutrition threshold is involved to each glowworm, then the spread operation is performed to produce offspring by using the method of normal distribution. Additionally, the individuals who continue to perform poorly are eliminated. The aforementioned operations can largely keep the diversity of the whole populations. After that, SBGSO is combined with rough set （RS） to handle the attribute reduction problem. When dealing with the attribute reduction problem, SBGSO is taken as a kind of search strategy and RS is taken as the evaluation criteria for attribute subsets. To analyze the feasibility and effectiveness of the proposed method, five UCI datasets are used to conduct experiments. Moreover, the 10-fold and SVM are involved to analyze the classification accuracy, experimental results show that the method has relatively higher reduction rate compared with other methods.

关 键 词：二元萤火虫算法 扩散机制 一维细胞自动机 粗糙集 属性约简 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]