电器与能效管理技术Issue(6)：64-69,79,7.DOI:10.16628/j.cnki.2095-8188.2024.06.010
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
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分类
信息技术与安全科学引用本文复制引用
马嘉晨,高松,王蕾..基于VarianceThreshold-GARFECV的特征选择方法[J].电器与能效管理技术,2024,(6):64-69,79,7.
