Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. This paper extends PDL toward the task of classification and proposes a meta-learning technique for inducing a PDL classifier by solving a suitably defined (binary) classification problem on a paired version of the original training data. We analyze the performance of the PDL classifier in a large-scale empirical study and find that it outperforms state-of-the-art methods in terms of prediction performance. Last but not least, we provide an easy-to-use and publicly available implementation of PDL in a Python package.

翻译：成对差异学习（PDL）是近期提出的一种用于回归问题的新型元学习技术。其核心思想并非以标准方式学习从实例到结果的映射，而是学习一个以两个实例作为输入、预测两者结果间差异的函数。给定此类函数，查询实例的预测可通过每个训练样本推导并取平均得出。本文拓展了PDL在分类任务中的应用，提出一种元学习技术：通过在原训练数据的成对版本上求解适当定义的（二分类）分类问题，从而构建PDL分类器。我们通过大规模实证研究分析了PDL分类器的性能，发现其在预测性能方面优于当前主流方法。最后，我们提供了易于使用且公开可用的PDL Python软件包实现。