北京科技大学学报2011，Vol.33Issue(12)：1550-1557,8.
基于协同进化机制的欠采样方法
Under-sampling method based on cooperative co-evolutionary mechanism
摘要
Abstract
For the bottleneck of improving the accuracy of minority class samples within the paradigm of imbalanced datasets,a novel under-sampling method based on the cooperative co-evolutionary mechanism was presented in this paper.During the employment of the method,the majority and the minority samples were divided into two populations,which adopted the cooperative co-evolutionary mechanism,dynamically adaptive crossovers and mutation operators to automatically adjust the evolution process within populations.Simulation results prove that the method enhances the capacity of local search,improves the distribution characteristics of populations and strengthens the capacity of global convergence.Moreover,the method notably improves the accuracy of the minority samples without degrading that of the majority ones.Compared to other classical resampling methods,the method shows good noise immunity with more powerful robustness.关键词
非平衡数据集/分类/采样/协同进化/自适应算法Key words
imbalanced datasets/classification/sampling/cooperative co-evolution/adaptive algorithms分类
信息技术与安全科学引用本文复制引用
翟云,杨炳儒,王树鹏,张德政,安冰..基于协同进化机制的欠采样方法[J].北京科技大学学报,2011,33(12):1550-1557,8.基金项目
国家高技术研究发展计划重大专项 （）
国家自然科学基金资助项目（61003260 （）
60875029 （）
61070101） （）
