摘要
为解决在数据不平衡条件下使用XGBoost框架处理二分类问题时算法对少数类样本的识别能力下降的问题,提出了基于代价敏感激活函数的XGBoost算法(Cost-sensitive Activation Function XGBoost,CSAF-XGBoost)。在XGBoost框架构建决策树时,数据不平衡会影响分裂点的选择,导致少数类样本被误分。通过引入代价敏感激活函数改变样本在不同预测结果下损失函数的梯度变化,来解决被误分的少数类样本因梯度变化小而无法在XGBoost迭代过程中被有效分类的问题。通过实验分析了激活函数的参数与数据不平衡度的关系,并对CSAF-XGBoost算法与SMOTE-XGBoost,ADASYN-XGBoost,Focal loss-XGBoost,Weight-XGBoost优化算法在UCI公共数据集上的分类性能进行了对比。结果表明,在F1值和AUC值相同或有提高的情况下,CSAF-XGBoost算法对少数类样本的检出率比最优算法平均提高了6.75%,最多提高了15%,证明了CSAF-XGBoost算法对少数类样本有更高的识别能力,且具有广泛的适用性。
For binary classification with category imbalance,acost-sensitive activation function XGBoost algorithm(CSAF-XGBoost)is proposed to promote the ability of recognizing minority samples.When XGBoost algorithm constructs decision trees,unbalanced data will affect split point selection,which lead to misclassification of minority.By constructing cost-sensitive activation function(CSAF),samples in different estimation are under different gradient variations,which approach the problem that the gradient variation of misclassified minority sample is too small to make samples be recognized correctly in iterations.The experiments analyze the relation of imbalanced rate(IR)to parameters,and compare performance with SMOTE-XGBoost,ADASYN-XGBoost,Focal loss-XGBoost and Weight-XGBoost on UCI datasets.As for recall rate of minority,CSAF-XGBoost surpasses the best methods 6.75%in average and 15%in maximum with F1-score and AUC score in the same level.The results prove CSAF-XGBoost has better performance in recognizing minority class samples and wider applicability.
作者
李京泰
王晓丹
LI Jing-tai;WANG Xiao-dan(Air and Missile Defense College,Air Force Engineering University,Xi’an 710051,China)
出处
《计算机科学》
CSCD
北大核心
2022年第5期135-143,共9页
Computer Science
