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首页> 外文期刊>Cybernetics, IEEE Transactions on >Projection-Based Ensemble Learning for Ordinal Regression
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Projection-Based Ensemble Learning for Ordinal Regression

机译：基于投影的集合学习用于序数回归

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摘要

The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper proposes an ensemble methodology specifically adapted to this type of problem, which is based on computing different classification tasks through the formulation of different order hypotheses. Every single model is trained in order to distinguish between one given class $(k)$ and all the remaining ones, while grouping them in those classes with a rank lower than $k$ , and those with a rank higher than $k$ . Therefore, it can be considered as a reformulation of the well-known one-versus-all scheme. The base algorithm for the ensemble could be any threshold (or even probabilistic) method, such as the ones selected in this paper: kernel discriminant analysis, support vector machines and logistic regression (LR) (all reformulated to deal with ordinal regression problems). The method is seen to be competitive when compared with other state-of-the-art methodologies (both ordinal and nominal), by using six measures and a total of 15 ordinal datasets. Furthermore, an additional set of experiments is used to study the potential scalability and interpretability of the proposed method when using LR as base methodology for the ensemble.
机译：将模式分类为自然有序的标签称为序数回归。本文提出了一种特别适用于此类问题的集成方法，该方法基于通过制定不同顺序的假设来计算不同的分类任务。每个模型都经过训练，以区分给定的类$（k）$和所有其余的类，同时将它们分组为等级低于$ k $的那些类别和等级高于$ k $的那些类别。因此，可以将其视为公知的“一对多”方案的重新表述。集成的基本算法可以是任何阈值（甚至概率性）方法，例如本文中选择的方法：内核判别分析，支持向量机和逻辑回归（LR）（所有方法都经过重新设计以处理序数回归问题）。与其他最新方法（序数和名义方法）相比，该方法被认为具有竞争力，通过使用六种度量值和总共15种序数数据集。此外，当使用LR作为整体的基本方法时，还使用另一组实验来研究所提出方法的潜在可扩展性和可解释性。

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