Feature selection aims to preprocess the target dataset, find an optimal and
most streamlined feature subset, and enhance the downstream machine learning
task. Among filter, wrapper, and embedded-based approaches, the reinforcement
learning (RL)-based subspace exploration strategy provides a novel objective
optimization-directed perspective and promising performance. Nevertheless, even
with improved performance, current reinforcement learning approaches face
challenges similar to conventional methods when dealing with complex datasets.
These challenges stem from the inefficient paradigm of using one agent per
feature and the inherent complexities present in the datasets. This observation
motivates us to investigate and address the above issue and propose a novel
Ansatz, namely HRLFS. Our methodology initially employs a Large Language
Model (LLM)-based hybrid state extractor to capture each feature’s mathematical
and semantic characteristics. Based on this information, features are
clustered, facilitating the construction of hierarchical agents for each
cluster and sub-cluster. Extensive experiments demonstrate the efficiency,
scalability, and robustness of our approach. Compared to contemporary or the
one-feature-one-agent RL-based approaches, HRLFS improves the downstream ML
performance with iterative feature subspace exploration while accelerating
total run time by reducing the number of agents involved.

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