粒子群优化覆盖算法  

Particle Swarm Optimization Covering Algorithm

作　　者：贾瑞玉[1] 宁再早[1] 

机构地区：[1]安徽大学计算机科学与技术学院,合肥230039

出　　处：《计算机工程》2011年第21期167-169,共3页Computer Engineering

基　　金：安徽省高等学校省级自然科学基金资助项目(KJ2011A006)

摘　　要：在覆盖算法中,识别精度与泛化能力之间存在矛盾。为此,结合粒子群优化(PSO)具有的全局搜索能力,提出一种PSO覆盖算法。将领域覆盖算法中每一类样本形成的一组覆盖转化为粒子群,并在迭代过程中搜索出较好的覆盖粒子,从而得到一组个数较少且分类效果较好的覆盖。实验结果表明,该算法具有较高的分类识别精度及较优的泛化能力。Aiming at the conflict between recognition accuracy and generalization ability.Considering the stronger search ability of Particle Swarm Optimization（PSO）,this paper introduces a PSO covering algorithm.The algorithm converts a group of coverings of each type of sample given with covering algorithm into a particle swarm,and searches for a better covering particle in the iteration process.It gets a set number of coverings,which is less but with better classification performance.Experimental results prove this algorithm has better classification accuracy and generalization capability.

关 键 词：粒子群优化 机器学习 覆盖算法 全局搜索 泛化 

分 类 号：TP311.52[自动化与计算机技术—计算机软件与理论]