Sampling-guided exploration of active feature selection policies

2026-03-16

AI Summary

This work addresses the trade-off between predictive performance and feature acquisition cost by proposing a dynamic, sample-level feature selection method. It formulates active feature selection as a Markov decision process with variable state dimensions and employs reinforcement learning to sequentially recommend the most cost-effective next feature. To enhance scalability and policy simplicity, the approach innovatively integrates a heuristic feature combination exploration strategy tailored for large-scale data and a post-fitting regularization mechanism, effectively compressing decision paths. Experimental results on four binary classification datasets—ranging up to 56 features and 4,500 samples—demonstrate that the proposed method achieves higher accuracy while significantly outperforming existing approaches in terms of decision efficiency and model compactness.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Determining the most appropriate features for machine learning predictive models is challenging regarding performance and feature acquisition costs. In particular, global feature choice is limited given that some features will only benefit a subset of instances. In previous work, we proposed a reinforcement learning approach to sequentially recommend which modality to acquire next to reach the best information/cost ratio, based on the instance-specific information already acquired. We formulated the problem as a Markov Decision Process where the state's dimensionality changes during the episode, avoiding data imputation, contrary to existing works. However, this only allowed processing a small number of features, as all possible combinations of features were considered. Here, we address these limitations with two contributions: 1) we expand our framework to larger datasets with a heuristic-based strategy that focuses on the most promising feature combinations, and 2) we introduce a post-fit regularisation strategy that reduces the number of different feature combinations, leading to compact sequences of decisions. We tested our method on four binary classification datasets (one involving high-dimensional variables), the largest of which had 56 features and 4500 samples. We obtained better performance than state-of-the-art methods, both in terms of accuracy and policy complexity.

Problem

• active feature selection
• feature acquisition cost
• instance-specific feature selection
• sequential decision making
• high-dimensional data

Innovation

• active feature selection
• reinforcement learning
• Markov Decision Process
• heuristic-based strategy
• post-fit regularization