电子学报2017，Vol.45Issue(11)：2695-2704,10.DOI:10.3969/j.issn.0372-2112.2017.11.017
基于互信息下粒子群优化的属性约简算法
An Attribute Reduction Algorithm Based on Mutual Information of Particle Swarm Optimization
摘要
Abstract
Minimum attribute reduction is the optimum problem in the attribute reduction of the rough sets theory.To seek the minimum attribute reduction,the attribute reduction algorithm based on the particle swarm optimization (ARPSO algorithm) beats the traditional attribute reduction algorithm.In existed ARPSO algorithms,the positive region is usually taken as the heuristic information,however,it is not precision enough to measure the uncertainty.The mutual information is a more efficient tool to measure the uncertainty in the rough sets theory.To handle this problem,an attribute reduction algorithm based on the particle swarm optimization takes the mutual information(MIPSO algorithm)as a term in the fitness function,The proposed MIPSO algorithm improves the regional shock search embedded particle swarm optimization algorithm (RSPSO) by enhancing the speed which the particle is close to the attractor,preventing from being local optimum early and finding the optimum as soon as possible.Consequently,the global convergence of the MIPSO algorithm is guaranteed as soon as possible.The experimental results show that the proposed MIPSO algorithm not only improves the optimization ability,accelerates the speed and improves the accuracy,but also can keep the mutual information value of all attributes before reducing approximately equal to the value of remaining attributes after reducing.关键词
互信息/粒子群优化/最小属性约简/粗糙集/局部搜索模式Key words
mutual information/particle swarm optimization/minimum attribute reduction/rough set/local search schemes分类
信息技术与安全科学引用本文复制引用
续欣莹,张扩,谢珺,谢刚..基于互信息下粒子群优化的属性约简算法[J].电子学报,2017,45(11):2695-2704,10.基金项目
人社部留学回国人员科技活动择优资助项目(No.2013-68) （No.2013-68）
山西省自然科学基金(No.2014011018-2) （No.2014011018-2）
山西省回国留学人员科研资助项目No.2013-033,No.2015-045) （）
