论文标题
在数据不平衡环境中加权损失的一致批准化
Consistent Batch Normalization for Weighted Loss in Imbalanced-Data Environment
论文作者
论文摘要
在这项研究中，考虑了基于馈电神经网络的分类问题。从不平衡的数据集中学习是机器学习领域中最重要的实践问题之一。基于成本敏感方法的加权损失函数（WLF）是一种不平衡数据集的众所周知有效的方法。在本研究中考虑了WLF和批发归一化（BN）的组合。在深度学习的最新发展中，BN被认为是一种强大的标准技术。两种方法的简单组合都会导致大小固有性问题，因为两种方法中对数据集有效大小的解释之间的解释之间的不匹配。提出了对BN的简单修改，称为加权BN（WBN），以纠正尺寸不匹配。 WBN的想法简单明了。使用数值实验验证了数据失衡环境中所提出的方法。
In this study, classification problems based on feedforward neural networks in a data-imbalanced environment are considered. Learning from an imbalanced dataset is one of the most important practical problems in the field of machine learning. A weighted loss function (WLF) based on a cost-sensitive approach is a well-known and effective method for imbalanced datasets. A combination of WLF and batch normalization (BN) is considered in this study. BN is considered as a powerful standard technique in the recent developments in deep learning. A simple combination of both methods leads to a size-inconsistency problem due to a mismatch between the interpretations of the effective size of the dataset in both methods. A simple modification to BN, called weighted BN (WBN), is proposed to correct the size mismatch. The idea of WBN is simple and natural. The proposed method in a data-imbalanced environment is validated using numerical experiments.
