基于VarianceThreshold-GARFECV的特征选择方法
Feature Selection Method Based on VarianceThreshold-GARFECV
马嘉晨 1高松 2王蕾2
作者信息
- 1. 东北电力大学 电气工程学院,吉林 吉林 132012
- 2. 国网吉林省电力有限公司 电力科学研究院,吉林 长春 130021
- 折叠
摘要
针对主动配电网风险初始特征子集存在冗余故障特征变量和非强相关变量的问题,提出一种基于 VarianceThreshold-GARFECV的特征选择方法.所提方法结合方差阈值和基于遗传算法的递归特征消除交叉验证(RFECV)技术,能够有效选择出最优的特征集合.实验结果表明,所提方法可以对配电网故障风险初始特征集合进行筛选和选择,剔除关联性弱和冗余的特征变量,从而达到降低配电网数据的复杂性、避免过拟合、增加模型的可解释性的目的,具有较高的准确率和稳定性.
Abstract
In view of the existence of redundant fault characteristic variables and non-strongly correlated variables in the initial feature subset of active distribution network risk,a feature selection method based on VarianceThreshold-GARFECV is proposed.The proposed method combines the variance threshold and the recursive feature cancellation cross-validation(RFECV)technology based on genetic algorithm,which can effectively select the optimal feature set.Experimental results show that the proposed method can screen and select the initial feature set of distribution network fault risk,and eliminate the characteristic variables with weak correlation and redundancy,so as to reduce the complexity of distribution network data,avoid overfitting,and increase the interpretability of the model,with high accuracy and stability.
关键词
特征选择/态势感知/风险预测/VarianceThresholdKey words
feature selection/situational awareness/risk prediction/VarianceThreshold引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据