Adaptive Kernel Learning via Meta-Reinforcement for Feature Selection

Abstract

The selection of an appropriate kernel function remains a significant challenge in kernel methods, frequently relying on manual tuning and heuristic approaches. This research introduces a novel framework employing meta-reinforcement learning to autonomously learn and adapt kernel functions. The system utilizes a reinforcement learning agent that interacts with a diverse library of kernels, receiving rewards based on classification accuracy. Through this interaction, the agent learns a policy for selecting kernels based on the characteristics of the input data, concurrently adjusting kernel parameters using an adaptive kernel learning algorithm. This approach offers a dynamic and automated solution, promising improved performance and reduced reliance on expert knowledge. The core idea is to treat kernel selection as a sequential decision-making problem, where the agent learns to choose the best kernel for a given task over time. The framework's adaptability allows it to generalize across different datasets and tasks, potentially uncovering kernel configurations previously unexplored. This work addresses the limitations of traditional kernel selection methods and presents a promising avenue for enhancing the effectiveness of kernel-based machine learning algorithms.