论文标题
通过虚拟大数据投影进行稳定的不平衡数据分类
Towards Stable Imbalanced Data Classification via Virtual Big Data Projection
论文作者
论文摘要
虚拟大数据（VBD）被证明是有效减轻模式崩溃和消失的发电机梯度，这是最近的两个主要问题，即最近的两个主要问题。在本文中，我们研究了VBD解决机器学习中其他两个主要挑战的能力，包括深度自动编码器培训和数据分类不平衡。首先，我们证明，VBD可以通过提供庞大的多元化训练数据来大大减少自动编码器的验证损失，这是更好地概括以最大程度地减少过度拟合问题的关键。其次，我们使用VBD提出了第一个基于投影的方法，称为跨偶联，以平衡偏斜的类别分布而不过度采样。我们证明，跨偶联可以解决数据驱动方法的不确定性问题，以实现分类不平衡的分类。
Virtual Big Data (VBD) proved to be effective to alleviate mode collapse and vanishing generator gradient as two major problems of Generative Adversarial Neural Networks (GANs) very recently. In this paper, we investigate the capability of VBD to address two other major challenges in Machine Learning including deep autoencoder training and imbalanced data classification. First, we prove that, VBD can significantly decrease the validation loss of autoencoders via providing them a huge diversified training data which is the key to reach better generalization to minimize the over-fitting problem. Second, we use the VBD to propose the first projection-based method called cross-concatenation to balance the skewed class distributions without over-sampling. We prove that, cross-concatenation can solve uncertainty problem of data driven methods for imbalanced classification.
