Deep learning methods can classify various unstructured data such as images, language, and voice as input data. As the task of classifying anomalies becomes more important in the real world, various methods exist for classifying using deep learning with data collected in the real world. As the task of classifying anomalies becomes more important in the real world, there are various methods for classifying using deep learning with data collected in the real world. Among the various methods, the representative approach is a method of extracting and learning the main features based on a transition model from pre-trained models, and a method of learning an autoencoderbased structure only with normal data and classifying it as abnormal through a threshold value. However, if the dataset is imbalanced, even the state-of-the-arts models do not achieve good performance. This can be addressed by augmenting normal and abnormal features in imbalanced data as features with strong distinction. We use the features of the autoencoder to train latent vectors from low to high dimensionality. We train normal and abnormal data as a feature that has a strong distinction among the features of imbalanced data. We propose a latent vector expansion autoencoder model that improves classification performance at imbalanced data. The proposed method shows performance improvement compared to the basic autoencoder using imbalanced anomaly dataset.
翻译:深层次的学习方法可以对各种非结构化的数据进行分类,例如图像、语言和语音等输入数据。随着异常现象的分类任务在现实世界变得更加重要,使用在现实世界中收集的数据进行分类的方法也各不相同。随着异常现象的分类任务在现实世界中变得更加重要,使用在现实世界中收集的数据进行分类的方法也各有不同。在各种方法中,代表性方法是一种根据从预科模型到高度的过渡模型提取和学习主要特征的方法,以及一种学习基于自动编码的结构的方法,这种结构只有以正常数据为基础,并通过阈值将其分类为异常。但是,如果数据集存在不平衡,即使状态的模型也没有取得良好的性能,那么,就有可能用在现实世界中收集的数据中增加正常和异常特征的方法进行分类。在各种方法中,我们使用自动编码的特征来从低度到高度的向高度的矢量来培训潜在的矢量。我们训练正常和异常数据作为特征的一种特征,只有正常的和异常的数据,并且通过临界值进行分类。但是,如果数据集存在偏差,即使数据,即使状态模型的状态模型没有达到良好的状态,那么,我们建议使用潜层的矢量的矢量性分析方法将改进。