  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.
