Pairwise Difference Learning for Classification
<|reference_start|>Pairwise Difference Learning for Classification: 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.<|reference_end|>
@article{belaid2024pairwise,
    title={Pairwise Difference Learning for Classification},
    author={Mohamed Karim Belaid, Maximilian Rabus and Eyke H"ullermeier},
    journal={arXiv preprint arXiv:2406.20031},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.20031},
    primaryClass={cs.LG cs.AI}
}