A Memory-Augmented Extension of Random Forest: The Memory-Augmented Forest (MAF)

As a classic machine learning algorithm based on the Bagging ensemble strategy, Random Forest has been widely utilized in both academia and industry due to its exceptional non-linear fitting capabilities, high robustness, and inherent resistance to overfitting. However, when faced with streaming data inputs, long-sequence dependent features, and scenarios requiring dynamic, adaptive predictive adjustments, traditional Random Forests reveal inherent limitations. Once the training phase of a standard Random Forest is complete, its tree structures and leaf node statistics become permanently fixed. Consequently, each forward prediction process remains static and isolated, lacking the ability to dynamically capture and update context information during inference. To address this challenge, this paper proposes a novel ensemble learning framework termed the Memory-Augmented Forest (MAF). Drawing inspiration from the core concepts of the memory mechanism in Transformer architectures, MAF integrates a global dynamic memory module directly into the prediction pipeline of the traditional Random Forest. During each prediction cycle, the input sample is first processed by the static forest base to extract high-dimensional feature representations. An attention-based addressing mechanism is then employed to read from the memory module, retrieving historical predictive contexts or global statistical supplements. Finally, based on the current prediction outcome and environmental feedback, the memory module is dynamically updated using a gated recurrent unit or an incremental update strategy similar to Transformer memories. This paper provides a rigorous mathematical formulation and theoretical analysis of the MAF architecture, covering its forward read operations, instant update mechanisms, convergence properties, and computational complexity. Theoretical investigations demonstrate that while MAF preserves the efficiency, high parallelism, and low variance advantages of traditional Random Forests, it equips the model with the capacity to dynamically adapt to shifting contexts. This mechanism effectively bridges the gap for ensemble learning in temporal modeling and dynamic evolutionary tasks, opening up a promising new avenue for fusing ensemble methods with deep learning memory architectures.