电子学报2012，Vol.40Issue(11)：2264-2269,6.DOI:10.3969/j.issn.0372-2112.2012.11.019
多目标优化算法在多分类中的应用研究
Research of Multi-Objective Optimization Algorithms' Application in Multi-Class Classification
摘要
Abstract
When Mute-objective Particle Swarm Optimization (MOPSO) optimizes the multi-objective problems of the mul-tiobjective simultaneous learning framework (MSCC),there are only a few nondominated solutions in MOPSO population. In this case,NSGA-II can keep a lot of good dominated solutions in the population, which will help the population optimize,so this paper brought in NSGA-Ⅱ as the optimization algorithm. The results of experiments show that, under the optimization of NSGA- Ⅱ, MSCC framework can get better multi-class classifiers. However, dominated solutions can get better classifiers than nondominated solutions. By observing the changing curves of the maximum classification accuracy rate following with the optimization of populations, this paper found that, when dealing with most of the data sets, the maximum accuracy is not improved following the optimization of populations .关键词
多分类/多目标优化/聚类/MOPSO/NSGA-ⅡKey words
multi-class/multi-objective optimization/cluster/MOPSO/NSGA-Ⅱ分类
信息技术与安全科学引用本文复制引用
尚荣华,胡朝旭,焦李成,白靖..多目标优化算法在多分类中的应用研究[J].电子学报,2012,40(11):2264-2269,6.基金项目
国家自然科学基金(No.61001202,No.61072139,No.61003199) （No.61001202,No.61072139,No.61003199）
中国博士后科学基金(No.201104658,No.20090451369,No.20090461283) （No.201104658,No.20090451369,No.20090461283）
陕西省自然科学基础研究计划(No.2010JQ8023,No.2011JQ8010) （No.2010JQ8023,No.2011JQ8010）
国家教育部博士点基金(No.20100203120008,No.20090203120016,No.200807010003) （No.20100203120008,No.20090203120016,No.200807010003）
高等学校学科创新引智计划(No.B07048) （No.B07048）
教育部"长江学者和创新团队发展计划"(No.IRT1170) （No.IRT1170）
