基于混合采样和改进随机森林的窃电检测

Electric theft detection based on hybrid sampling and improved random forest

针对窃电检测中存在的数据不平衡和分类器效率低的问题,提出一种基于混合采样和随机森林的窃电检测方法。首先,用随机森林模型的误分率作为SMOTE算法的重采样率,提出E-SMOTE算法;其次,在E-SMOTE和Tome Links混合采样的过程中,引入模型ROC曲线下方的面积(area under curve,AUC)作为迭代停止的条件,实现用电数据集的平衡;最后,用基于马修斯相关系数(Matthews correlation coefficient,MCC)的置换法和卡方检验进行特征选择,并在传统的随机森林模型中引入Q统计值进行选择性集成,不仅优化属性特征的选择,还提升随机森林模型的多样性。实验结果表明:提出的混合采样算法较优于7种常用采样方法,改进的随机森林模型也在精确率、特异度和F1分数等多项指标中表现出更优的性能。

Targeting the problems of data imbalance and low efficiency of classifiers in power theft detection,a power theft detection method based on hybrid sampling and random forest is proposed.Firstly,the error rate of the random forest model is used as the re-sampling rate of the SMOTE algorithm,and the E-SMOTE algorithm is proposed.Secondly,during the mixed sampling of E-SMOTE and Tome Links,the area under the ROC curve(AUC) of the model is introduced as a condition for the iteration to stop to achieve the balance of the electricity data set.Finally,the permutation method based on Matthews correlation coefficient(MCC) and the chi-square test are used for feature selection,and the Q statistics is introduced into the traditional random forest model for selective integration,which not only optimizes the selection of attribute features,but also improves the diversity of the random forest model.The experimental results show that the proposed hybrid sampling algorithm is better than 7 common sampling methods,and the improved random forest model also shows better performance in accuracy,specificity and F1 score.