Pairwise difference learning improves classification with uncertainty estimates
Uncertainty Quantification in Pairwise Difference Learning for Classification
Machine LearningArtificial Intelligence
Summary
Predicting what category something belongs to can be tricky, especially when the system isn't sure about its guess. This paper looks at a new way of making these predictions by comparing pairs of examples instead of just looking at one. The researchers also worked out how to measure how confident the system is about its choices, splitting uncertainty into two types. They tested their approach on large datasets to see how well it both predicts and expresses uncertainty.
What this means in practice
- •For machine learning engineers: Enhance classification models by integrating pairwise difference learning that provides reliable uncertainty estimates alongside predictions.
- •For risk assessment teams: Use uncertainty-aware classifiers to better gauge confidence in automated decision systems for sensitive areas like finance or healthcare.
Authors
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Abstract
Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) 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. We propose a version of PDL for the task of classification and propose a suitable meta-learning technique for this purpose. Moreover, we address the problem of uncertainty quantification, i.e., of representing and quantifying the uncertainty of a PDL classifier's predictions in a faithful manner. Building on recent work on the decomposition of total uncertainty into aleatoric and epistemic components, we propose different ways of extending corresponding measures to the setting of PDL, depending on how and to what extent uncertainty is represented by the underlying base learner. In large-scale empirical studies, we analyze both the predictive accuracy and the uncertainty awareness of our PDL classifier.