基于改进随机森林算法的停电敏感用户分类

目前，我国电网企业对于识别停电投诉风险，开展用户停电敏感程度分析的研究工作还处在起步阶段.为了有效地分析停电用户的敏感程度，提出了一种基于改进随机森林算法的停电敏感用户分类算法.首先，对原始数据进行清洗、特征选择等预处理；接着，采用SMOTE算法增加少数敏感用户样本数据量，解决数据分布不均匀问题；然后，以Fisher比作为特征的重要性度量，按比例随机采样选取具有代表性的特征构成子特征空间；最后，利用随机森林算法识别停电敏感用户.通过在真实停电数据上的实验，验证了提出的方法不仅具有较好的准确性和时间性能，而且可以有效处理高维、冗余特征的数据.

Power Outage Sensitive Customers Classification Based on Improved Random Forest Algorithm

At present, the research on the risk identification of power outage complaints and the customer sensitivity analysis in power grid companies is at its early stage. In order to effectively analyze the sensitivity of power outage customers, a sensitive customer classification algorithm based on the improved random forest algorithm is proposed. First, the data is preprocessed by methods of data cleaning, feature selection, and so on. Second, the SMOTE algorithm is used to increase the number of sensitive customers to solve the problem of data imbalance. Third, the representative feature space is selected by proportional random sampling. The Fisher ratio is used as the characteristic importance measure. Then, the random forest algorithm is used to recognize the customers that are sensitive to power outage. Finally, the experiments on real power outage data show that the proposed method not only has better accuracy and time performance but also can effectively deal with high-dimensional data with redundant features.