A Multi-Component Reward Function with Policy Gradient for Automated Feature Selection with Dynamic Regularization and Bias Mitigation

2025-10-09

AI Summary

Traditional static feature exclusion strategies fail to mitigate model bias arising from hidden dependencies. This paper proposes an end-to-end reinforcement learning framework that jointly optimizes fairness-aware automated feature selection and predictive modeling. Methodologically, we formulate a multi-objective reward function balancing accuracy and fairness; design a policy-gradient-based action space over feature subsets; and incorporate dynamic regularization and ensemble fusion for adaptive feature selection. Experiments across multiple benchmark datasets demonstrate that our approach maintains high predictive accuracy while significantly reducing bias—e.g., Equalized Odds difference decreases by up to 42%—and improves model generalization and robustness. The core contribution lies in the first integration of fairness constraints directly into the feature selection decision process, thereby overcoming the limitations of static exclusion.

Technology Category

Machine Learning: Dimensionality Reduction/Feature SelectionComputer Vision: Bias, Fairness & PrivacyPhilosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSocial Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection within a single learning process. Unlike traditional heuristic-driven filter or wrapper approaches, our RL agent adaptively selects features using a reward signal that explicitly integrates predictive performance with fairness considerations. This dynamic formulation allows the model to balance generalization, accuracy, and equity throughout the training process, rather than rely exclusively on pre-processing adjustments or post hoc correction mechanisms. In this paper, we describe the construction of a multi-component reward function, the specification of the agents action space over feature subsets, and the integration of this system with ensemble learning. We aim to provide a flexible and generalizable way to select features in environments where predictors are correlated and biases can inadvertently re-emerge.

Problem

• Automated feature selection with dynamic regularization and bias mitigation
• Preventing bias from hidden dependencies in model predictions
• Balancing generalization, accuracy, and equity during training

Innovation

• Reinforcement learning automates feature selection dynamically
• Multi-component reward balances accuracy and fairness
• Integrated framework mitigates bias during training process