论文标题
部分可观测时空混沌系统的无模型预测
A Deep Learning Anomaly Detection Method in Textual Data
论文作者
论文摘要
在本文中，我们建议使用深度学习和变压器体系结构与经典的机器学习算法结合使用，以检测和识别文本中的文本异常。深度学习模型提供了有关文本数据的非常关键的上下文信息，所有文本上下文都将转换为数值表示。我们使用多种机器学习方法，例如句子变形金刚，自动编码器，逻辑回归和距离计算方法来预测异常。该方法对文本数据进行了测试，我们使用了来自原始文本中的不同源的句法数据作为异常，或将其用作目标。在异常检测领域中解释了不同的方法和算法，并提出了最佳技术的结果。这些结果表明，与我们正在测试的其他异常检测方法相比，我们的算法可能有可能降低假阳性率。
In this article, we propose using deep learning and transformer architectures combined with classical machine learning algorithms to detect and identify text anomalies in texts. Deep learning model provides a very crucial context information about the textual data which all textual context are converted to a numerical representation. We used multiple machine learning methods such as Sentence Transformers, Auto Encoders, Logistic Regression and Distance calculation methods to predict anomalies. The method are tested on the texts data and we used syntactic data from different source injected into the original text as anomalies or use them as target. Different methods and algorithm are explained in the field of outlier detection and the results of the best technique is presented. These results suggest that our algorithm could potentially reduce false positive rates compared with other anomaly detection methods that we are testing.
