Learning ordinal data
Learning ordinal data
复制标题
学习序数数据
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Xingye Qiao
中科院分区：
文献类型：
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作者：
Xingye Qiao
分类是统计学习中的一个重要课题。分类的目标是从训练数据集为观测的类别标签建立预测模型。通常假设类标签是无序的。然而，在许多实际应用中，类标签之间存在着内在的顺序关系。这些例子包括早期、中级和晚期的癌症患者，分为低、中、高信用级别的客户，以及富含不同数量细菌的实验对象。本文主要研究有序数据的分类问题，并介绍了该问题的理论框架。我们回顾了学习有序数据的传统方法和现代方法。特别是，我们强调了模型灵活性和可解释性之间的权衡。最后，我们讨论了关于有序数据学习的一些问题，包括针对这个问题的适当的损失函数。WIRESS Comput Stat 2015，7：341-346。DOI：10.1002/wics.1357
Classification is an important topic in statistical learning. The goal of classification is to build a predictive model from the training dataset for the class label of an observation. It is commonly assumed that the class labels are unordered. However, in many real applications, there exists an intrinsic ordinal relation between the class labels. Examples of these include cancer patients grouped in early, mediocre, and terminal stages, customers grouped into low, middle, and high credit levels, and experimental subjects enriched with different amounts of bacterial. In this article, we focus on the classification problem for ordinal data and introduce the theoretical setup of the problem. We review both traditional and modern methods in learning ordinal data. In particular, we emphasize the trade‐off between model flexibility and interpretability. Lastly, we discuss some issues regarding ordinal data learning, including an appropriate loss function for this problem. WIREs Comput Stat 2015, 7:341–346. doi: 10.1002/wics.1357