Ordinal Hyperplane Loss
Ordinal Hyperplane Loss
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DOI：
10.1109/bigdata.2018.8622079
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
B. Vanderheyden;Ying Xie
中科院分区：
文献类型：
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作者：
B. Vanderheyden;Ying Xie
顺序分类的问题出现在大量和越来越多的领域。一些最常见的来源和应用程序的有序数据包括评级尺度，医疗分类尺度，社会经济规模，有意义的连续数据分组，面部情绪强度，面部年龄估计等的预测有序类的问题通常是通过执行n-1二进制分类n个有序类或治疗有序类作为连续值回归。然而，第一种策略没有充分利用类的排序信息，第二种策略对有序类施加了强连续性假设。在本文中，我们提出了一种新的损失函数，称为有序超平面损失（OHPL），是专门为有序类数据设计的。OHPL的提出是预测有序类数据的一个重大进步，因为它使深度学习技术能够应用于结构化和非结构化数据的有序分类问题。通过最小化OHPL，深度神经网络学习将数据映射到最佳空间，在该空间中，点与其类质心之间的距离最小化，同时保持类之间的非平凡顺序关系。实验结果表明，具有OHPL的深度神经网络不仅在分类精度上优于最先进的替代方案，而且可以很好地扩展到大顺序分类问题。
The problem of ordinal classification occurs in a large and growing number of areas. Some of the most common source and applications of ordinal data include rating scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity, facial age estimation, etc. The problem of predicting ordinal classes is typically addressed by either performing n-1 binary classification for n ordinal classes or treating ordinal classes as continuous values for regression. However, the first strategy doesn’t fully utilize the ordering information of classes and the second strategy imposes a strong continuous assumption to ordinal classes. In this paper, we propose a novel loss function called Ordinal Hyperplane Loss (OHPL) that is particularly designed for data with ordinal classes. The proposal of OHPL is a significant advancement in predicting ordinal class data, since it enables deep learning techniques to be applied to the ordinal classification problem on both structured and unstructured data. By minimizing OHPL, a deep neural network learns to map data to an optimal space where the distance between points and their class centroids are minimized while a nontrivial ordinal relationship among classes are maintained. Experimental results show that deep neural network with OHPL not only outperforms the state-of-the-art alternatives on classification accuracy but also scales well to large ordinal classification problems.