Pairwise Difference Learning for Classification

• URL: http://arxiv.org/abs/2406.20031v1
• Date: Fri, 28 Jun 2024 16:20:22 GMT
• Title: Pairwise Difference Learning for Classification
• Authors: Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier,
• Abstract summary: Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. We extend PDL toward the task of classification by solving a suitably defined (binary) classification problem on a paired version of the original training data. We provide an easy-to-use and publicly available implementation of PDL in a Python package.
• Score: 19.221081896134567
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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