Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning (2504.17356v1)

Abstract: 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 approach, namely HRLFS. Our methodology initially employs a 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.

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

The paper "Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning" introduces an innovative approach for feature selection in complex datasets. This paper acknowledges the challenges that arise in conventional feature selection methodologies and presents a hierarchical reinforcement learning framework, designated as HRLFS, to improve efficiency and performance.

Problem Statement

Feature selection is vital in reducing dimensionality for machine learning tasks, enhancing model performance, and improving computational efficiency. Traditional feature selection methods, including filter, wrapper, and embedded approaches, each encounter distinct challenges when addressing complex datasets. Filter methods are often fast but overlook interactions between features. Wrappers provide a more detailed exploration but are computationally expensive, especially with large feature sets. Embedded approaches integrate selection directly into model training but lack flexibility across different models. Recent attempts to leverage reinforcement learning (RL) for feature selection offer promising results, yet existing RL methodologies face difficulties owing to inefficient paradigms that utilize one agent per feature.

Methodology

The HRLFS framework is built on a hierarchical reinforcement learning architecture designed to comprehend feature traits, divide them into manageable clusters, and conquer feature selection through intelligent exploration.

1. Hybrid Feature State Extraction:

The paper introduces a unique hybrid feature state extraction method. Utilizing concepts from Gaussian Mixture Models (GMM) and LLMs, it develops a dual-faceted feature representation. The GMM captures the numerical characteristics of features, while LLMs glean semantic insights from feature metadata. This hybrid state empowers clustering processes and informs the hierarchical agent system, enabling more accurate decision-making.

2. Hierarchical Agent Architecture:

HRLFS employs a novel comprehend-divide-and-conquer structure. Features are initially clustered based on their mathematical and semantic properties. This clustering informs the creation of hierarchical agents, each responsible for decisions within specific clusters and sub-clusters, reducing the number of active agents and associated computational demands during feature selection tasks.

3. Exploration and Optimization:

An iterative process where these hierarchical agents explore possible feature subsets and optimize their selection policies through reinforcement learning. A sophisticated reward structure balances model performance against feature quantity suppression, encouraging compact feature sets without sacrificing predictive capability.

Experimental Analysis

Extensive experiments across multiple domains (e.g., classification, regression) showcase HRLFS's robustness, efficiency, and improved predictive performance compared to existing methods such as KBest, LASSONet, GAINS, and SARLFS. Notably, HRLFS achieves better performance in feature selection while significantly reducing runtime. For instance, HRLFS outperformed SARLFS in terms of both quality of selected features and computational efficiency, demonstrating over 30% reduction in time consumption across various datasets. This is particularly pronounced in high-dimensional scenarios, underscoring HRLFS's scalability and adaptability.

Implications and Future Work

HRLFS introduces a scalable method for feature selection that leverages advanced reinforcement learning techniques to handle complex datasets efficiently. Practically, its deployment could enhance data preprocessing in large-scale machine learning tasks, facilitating improved predictive modeling with reduced computational overhead. Theoretically, HRLFS’s approach invites further exploration into hierarchical agent cooperation for decision-making processes, suggesting potential expansions into other areas such as automated machine learning (AutoML).

Future research could examine integrating generative models to assess simulated dataset implications, further refining feature state extraction methodologies. Additionally, exploring dynamic adjustments in hierarchical structures based on real-time data feedback might offer strategies to enhance even further the adaptability and efficacy of HRLFS in a broader range of applications.

In conclusion, HRLFS represents a significant advancement in the domain of feature selection, offering a viable path forward in managing complexities inherent in large, high-dimensional datasets and opening new possibilities in the intersection of reinforcement learning and feature optimization strategies.