School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.
Neural Comput. 2012 Nov;24(11):2825-51. doi: 10.1162/NECO_a_00358. Epub 2012 Aug 24.
Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. In ordinal LVQ, unlike in nominal LVQ, the class order information is used during training in selecting the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype-based models in general are more amenable to interpretations and can often be constructed at a smaller computational cost than alternative nonlinear classification models. Experiments demonstrate that the proposed ordinal LVQ formulations compare favorably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.
许多模式分析问题需要将示例分类为自然有序的类别。在这种情况下，标称分类方案将忽略类别顺序关系，这可能对分类准确性产生不利影响。本文介绍了两种新颖的有序学习矢量量化（LVQ）方案，具有度量学习，专门用于将数据项分类为有序类别。在有序 LVQ 中，与标称 LVQ 不同，在训练过程中使用类别顺序信息来选择要适应的类别原型，以及确定原型更新的确切方式。基于原型的模型通常更易于解释，并且通常可以以比替代非线性分类模型更小的计算成本来构建。实验表明，所提出的有序 LVQ 公式与标称同类产品相比具有优势。此外，我们的方法与现有的基准有序回归模型相比具有竞争力。
