Preservation of Feature Stability in Machine Learning Under Data Uncertainty for Decision Support in Critical Domains
This addresses the need for explainable and stable ML inputs in critical domains like healthcare or finance, though it appears incremental by comparing existing methods.
The paper tackled the problem of feature stability in machine learning under data uncertainty for critical decision support, finding that a descriptive classification method maintains higher accuracy and stable feature selection as data incompleteness increases.
In a world where Machine Learning (ML) is increasingly deployed to support decision-making in critical domains, providing decision-makers with explainable, stable, and relevant inputs becomes fundamental. Understanding how machine learning works under missing data and how this affects feature variability is paramount. This is even more relevant as machine learning approaches focus on standardising decision-making approaches that rely on an idealised set of features. However, decision-making in human activities often relies on incomplete data, even in critical domains. This paper addresses this gap by conducting a set of experiments using traditional machine learning methods that look for optimal decisions in comparison to a recently deployed machine learning method focused on a classification that is more descriptive and mimics human decision making, allowing for the natural integration of explainability. We found that the ML descriptive approach maintains higher classification accuracy while ensuring the stability of feature selection as data incompleteness increases. This suggests that descriptive classification methods can be helpful in uncertain decision-making scenarios.