基于协同进化机制的欠采样方法

Under-sampling method based on cooperative co-evolutionary mechanism

针对非平衡数据集分类中"少数类样本精度难以提高"这一瓶颈问题,提出了一种基于协同进化机制的欠采样方法.此方法将少数类样本与多数类样本划分为两类种群,采用种群协同进化原理,利用提出的动态交叉变异算子自适应协同进化过程,实现种群间自动调节和自动适应.仿真试验结果表明,此采样方法增强了局部随机搜索能力,改善了种群的分布特性,加强了算法的全局收敛能力,在不降低多数类样本分类性能的基础上有效提高了少数类样本的精度.与其他经典重采样方法相比,本文办法抗噪能力好,具有更强的鲁棒性.

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.