Noise in training data increases the tendency of many machine learning methods to overfit the training data, which undermines the performance. Outliers occur in big data as a result of various factors, including human errors. In this work, we present a novel discriminator model for the identification of outliers in the training data. We propose a systematic approach for creating training datasets to train the discriminator based on a small number of genuine instances (trusted data). The noise discriminator is a convolutional neural network (CNN). We evaluate the discriminator’s performance using several benchmark datasets and with different noise ratios. We inserted random noise in each dataset and trained discriminators to clean them. Different discriminators were trained using different numbers of genuine instances with and without data augmentation. We compare the performance of the proposed noise-discriminator method with seven other methods proposed in the literature using several benchmark datasets. Our empirical results indicate that the proposed method is very competitive to the other methods. It actually outperforms them for pair noise.

中文翻译：

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基于卷积神经网络的鉴别器

训练数据中的噪声增加了许多机器学习方法过度拟合训练数据的趋势，从而降低了性能。由于各种因素（包括人为错误），大数据中会出现异常值。在这项工作中，我们提出了一种新颖的鉴别模型，用于识别训练数据中的异常值。我们提出了一种用于创建训练数据集的系统方法，以基于少量真实实例（可信数据）来训练鉴别器。噪声鉴别器是卷积神经网络（CNN）。我们使用多个基准数据集和不同的噪声比来评估鉴别器的性能。我们在每个数据集中插入了随机噪声，并训练了辨别器对其进行清理。使用和不使用数据扩充的不同数量的真实实例对不同的鉴别器进行了训练。我们使用几种基准数据集比较了所提出的噪声鉴别器方法与文献中提出的其他七种方法的性能。我们的经验结果表明，所提出的方法与其他方法相比具有很强的竞争力。实际上，在配对噪声方面，它们的性能要优于它们。