论文标题
Thor：序数回归的基于阈值的排名损失
THOR: Threshold-Based Ranking Loss for Ordinal Regression
论文作者
论文摘要
在这项工作中，我们提出了一种基于回归的序数回归算法，用于将实例分类为顺序类别。与以前的方法相反，在这项工作中，类别之间的决策边界是预定义的，并且该算法学会根据这些预定义的边界将输入示例投射到其适当的分数上。这是通过添加新型阈值的成对损耗函数来实现的，该函数旨在最大程度地减少回归误差，从而最大程度地减少了平均绝对误差（MAE）度量。我们使用CNN-FRAMEWORK实现了我们建议的体系结构方法，以提取功能提取。对五个现实世界基准的实验结果表明，与最先进的序数回归算法相比，所提出的算法获得了最佳的MAE结果。
In this work, we present a regression-based ordinal regression algorithm for supervised classification of instances into ordinal categories. In contrast to previous methods, in this work the decision boundaries between categories are predefined, and the algorithm learns to project the input examples onto their appropriate scores according to these predefined boundaries. This is achieved by adding a novel threshold-based pairwise loss function that aims at minimizing the regression error, which in turn minimizes the Mean Absolute Error (MAE) measure. We implemented our proposed architecture-agnostic method using the CNN-framework for feature extraction. Experimental results on five real-world benchmarks demonstrate that the proposed algorithm achieves the best MAE results compared to state-of-the-art ordinal regression algorithms.
