Adaptive Machine Learning Models for Real-Time Decision Support: A Survey

Abstract

This survey provides a comprehensive overview of adaptive machine learning models for real-time decision support systems. In dynamic environments where data distributions can change over time, traditional static models often become obsolete, leading to degraded performance. Adaptive models, which can learn and evolve from continuous data streams, offer a promising solution. This paper categorizes and discusses various adaptive machine learning techniques, including online learning, continual learning, and reinforcement learning. We explore their application across diverse domains such as finance, healthcare, and autonomous systems. Furthermore, we identify and analyze the key challenges in the deployment and maintenance of these models, including concept drift, catastrophic forgetting, and the need for model interpretability. Finally, we highlight emerging trends and future research directions that are poised to shape the future of adaptive real-time decision support.