Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
This work addresses uncertainty metric selection for decision-making in healthcare AI, but it is incremental as it compares two existing metrics rather than introducing a new method.
The paper tackled the problem of selecting better uncertainty metrics for ensemble learning in healthcare AI, showing that ensemble mean is preferable to ensemble variance under mild assumptions, with empirical validation on diabetic retinopathy diagnosis.
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learners are combined to obtain a joint decision. Recently, various methods have been explored in literature for estimating decision uncertainties using ensemble learning; however, determining which metrics are a better fit for certain decision-making applications remains a challenging task. In this paper, we study the following key research question in the selection of uncertainty metrics: when does an uncertainty metric outperforms another? We answer this question via a rigorous analysis of two commonly used uncertainty metrics in ensemble learning, namely ensemble mean and ensemble variance. We show that, under mild assumptions on the ensemble learners, ensemble mean is preferable with respect to ensemble variance as an uncertainty metric for decision making. We empirically validate our assumptions and theoretical results via an extensive case study: the diagnosis of referable diabetic retinopathy.