摘要
粒的数量和分类错误率是粒计算互相冲突的两个目标,同时最小化这两个目标是不可能的.针对此,构造了多目标优化问题,分别建立分类超盒粒数量和训练错误率两个目标,通过多目标演化算法对该多目标优化问题进行求解,从而产生一系列分类超盒粒集.随机产生初始种群,多目标演化算法通过利用演化操作和反复迭代的方法,得到供用户选取不同性能的解集.
Granule number and classification error rate are two conflicting objectives in granular computing , it is impossible to minimize the two objectives simultaneously .The multi-objective optimization including the number of granule number and classification error was formed and solved by multi-objective evolutionary algorithm , and a series of multi-hyperbox granule sets were achieved .The multi-objective evolutionary algorithm obtained the different solution set by initialization of population , evolution operation and iteration method .Users can select the solution according to their requirements .
出处
《信阳师范学院学报(自然科学版)》
CAS
北大核心
2014年第1期127-130,共4页
Journal of Xinyang Normal University(Natural Science Edition)
基金
河南省基础研究与前沿技术项目(132300410421
132300410422)
河南省教育厅科学技术研究重点项目(13B520267)
河南省教育厅信息技术研究项目(ITE12155)
信阳师范学院青年基金项目
信阳师范学院青年骨干教师资助计划
关键词
粒计算
多目标优化
超盒粒
Pareto前端
granular computing
multi-objective optimization
hyperbox granule
Pareto front