Pairwise Difference Learning for Classification

TL;DR

This paper extends pairwise difference learning (PDL) from regression to classification, proposing a new meta-learning approach that predicts class differences between pairs of instances, demonstrating superior performance in large-scale experiments.

Contribution

The paper introduces a novel PDL-based classifier for classification tasks, adapting the pairwise difference concept and providing a practical Python implementation.

Findings

Outperforms state-of-the-art classifiers in empirical tests

Effective in large-scale classification problems

Provides accessible Python package for PDL

Abstract

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.