Department of Quantitative Methods, Universidad Loyola Andalucía, 14004 - Córdoba, Spain.
Department of Computer Science and Numerical Analysis, University of Córdoba, 14070 - Córdoba, Spain.
Neural Netw. 2016 Dec;84:57-66. doi: 10.1016/j.neunet.2016.08.004. Epub 2016 Aug 25.
Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.
有序分类考虑了那些标签变量遵循给定顺序的分类问题。在这类问题中，自然地，标记数据是稀缺的或难以获得的，因为在许多情况下，有序标签是由用户或专家给出的（例如在推荐系统中）。首先，本文提出了一种新的有序分类策略，该策略在模型构建步骤中同时使用有标签和无标签数据（一种被称为半监督学习的方案）。更具体地说，扩展了核判别学习的有序版本，以考虑无标签数据的邻域信息，建议在核函数诱导的特征空间中计算该邻域信息。其次，在有序分类的背景下，设计了一种新的半监督核学习方法，该方法与我们开发的分类策略相结合，以优化核参数。在 30 个数据集的一系列实验中，比较了有序分类中半监督学习的 6 种不同方法，结果表明：（1）判别分析的有序版本和使用无标签数据之间具有良好的协同作用；（2）在核函数诱导的特征空间中计算距离的优势。
