Range Based Confusion Matrix for Imbalanced Time Series Classification
Range Based Confusion Matrix for Imbalanced Time Series Classification
复制标题
用于不平衡时间序列分类的基于范围的混淆矩阵
DOI：
10.1109/cdma47397.2020.00006
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Arturo Del Valle
中科院分区：
文献类型：
--
作者：
Xianzhe Zhou;Arturo Del Valle
最近大量的机器生成数据允许检测相应系统中的异常行为，这在以前是不可能的。因此，异常检测问题在工业环境中变得越来越重要。这些数据通常具有时间序列性质，当使用机器学习方法时，标签是不平衡的。人们发现经典的点对点混淆矩阵对于模型性能评分具有误导性。我们提出了一种基于众所周知的混淆矩阵的增强方法来评估不平衡时间序列数据集的二元分类。该方法用于一个项目，该项目旨在预测向真实客户提供 Web 服务的服务器中的应用程序故障，并改进分类模型的估计
The recent flood of machine generated data allows for the detection of anomalous behaviors in the corresponding systems, something previously impossible. Consequently, the anomaly detection problem has grown in importance in industrial settings. This data often has a time-series nature and when a machine learning approach is used the labels are unbalanced. Classical point-to-point confusion matrices have been found misleading for scoring model performance. We propose an enhanced approach, based on the well-known confusion matrix, to evaluate binary classification on imbalanced time series datasets. This approach is utilized in a project which seeks to predict application failures in servers that provide web services to real customers and it results in improved estimates of classification models
DOI：
10.1145/1889681.1889687
发表时间：
2011-01-01
影响因子：
5
作者：
Ward, Jamie A.;Lukowicz, Paul;Gellersen, Hans W.
通讯作者：
Gellersen, Hans W.