论文标题
SDAE检测异常
Anomaly Detection with SDAE
论文作者
论文摘要
异常检测是学习校正和/或删除错误数据的重要数据预处理步骤。通过使用自动编码器来自动化此数据类型可以通过隔离通过手动或基本统计分析遗漏的异常来提高数据集的质量。对Ashrae Building Energy数据集进行了简单，深层和监督的深度自动编码器的训练并进行了比较。鉴于训练模型的受限参数，深度自动编码器最佳构造是最好的，但是，当给出了对测试数据集的考虑时，监督的深度自动编码器的表现优于检测到的其他完全异常模型。
Anomaly detection is a prominent data preprocessing step in learning applications for correction and/or removal of faulty data. Automating this data type with the use of autoencoders could increase the quality of the dataset by isolating anomalies that were missed through manual or basic statistical analysis. A Simple, Deep, and Supervised Deep Autoencoder were trained and compared for anomaly detection over the ASHRAE building energy dataset. Given the restricted parameters under which the models were trained, the Deep Autoencoder perfoms the best, however, the Supervised Deep Autoencoder outperforms the other models in total anomalies detected when considerations for the test datasets are given.
