基于迁移学习的电力通信网异常站点业务数量预测

Method Based on Transfer Learning for Predicting Quantity of Service in Power Communication Network

现有的多源迁移学习算法对回归问题的研究极少,大多是解决对称的二分类问题,本文提出了加权多源TrAdaBoost的回归算法,其中误差容忍系数能一定程度解决源领域样本权重缩减过快的问题,提高了算法的效果。在修改后的Friedman#1回归问题上进行了实验,验证了该算法的有效性,误差容忍系数可以提高大约0.01的R2分数。将该算法应用到电力通信网的行业问题中,提出了异常站点(业务数量缺失严重的站点)检测与真值预测模型,在特征工程中使用了社交网络分析的方法,充分考虑了站点在拓扑图中的重要性。最终的实验效果进一步验证了算法的有效性。

The existing multi-source transfer learning algorithms have very few researches on regression problems,and most of them are symmetric two-class classification problems.This paper presents a weighted multi-source TrAdaBoost regression algorithm,in which the error tolerance coefficient is proposed to solve the problem that the sample weight of the source domain is reduced too quickly,thus the effect of the algorithm is improved.Experiments are performed on the modified Friedman#1 regression problem to verify the effectiveness of the algorithm.The error tolerance coefficient can increase the R2 score by approximately 0.01.In this paper,the proposed algorithm is applied to the industry problems of power communication networks,and the anomaly site(sites with a large number of missing services)detection and true value prediction models are proposed.Moreover,the social network analysis methods are used in the feature engineering,and the importance of the site in the topology is fully considered.Finally,experimental results verify the effectiveness of the algorithm.