TLDR: This research introduces CAPS, a framework for automated feature selection that uses permutation-invariant embeddings and reinforcement learning to find optimal feature subsets, overcoming limitations of traditional methods. It extends to FedCAPS, a federated version that aggregates knowledge from decentralized clients without sharing sensitive raw data, using a sample-aware weighting strategy to handle data heterogeneity and ensure privacy.
The paper introduces a new approach to feature selection, a crucial step in machine learning that helps improve model performance and reduce computational costs by identifying and removing redundant or irrelevant data features. Traditional methods often struggle with complex feature interactions and adapting to different scenarios. Recent advancements using generative AI have helped, but they still face limitations like sensitivity to the order of features (permutation sensitivity) and assumptions about the data’s structure (convexity assumptions) that don’t always hold true in real-world applications.
This research addresses these challenges by presenting a novel framework called CAPS (Continuous optimization for feAture selection by integrating Permutation-invariant embeddings with a policy-guided Search strategy). The core idea is to learn a “permutation-invariant” representation of feature subsets. This means that no matter how the features within a selected subset are ordered, the system recognizes them as the same, eliminating a common source of bias. To achieve this, an encoder-decoder module is used, which maps feature subsets into a continuous embedding space and can reconstruct them back. This module uses a self-attention mechanism to focus on relationships between features rather than their sequence. To handle large datasets efficiently, it incorporates “inducing points” to reduce the computational complexity of attention calculations.
Once this intelligent embedding space is created, a policy-guided search strategy, powered by reinforcement learning (specifically, Proximal Policy Optimization or PPO), is deployed. This agent explores the learned embedding space to find the best possible feature subsets. It’s designed to optimize for two objectives simultaneously: maximizing the performance of the downstream machine learning task and minimizing the number of features selected. This approach is particularly effective because it doesn’t rely on the restrictive convexity assumptions that often limit gradient-based search methods, allowing it to navigate complex, non-convex spaces more effectively and avoid getting stuck in suboptimal solutions.
Extending to Federated Learning: FedCAPS
Recognizing that real-world data is often distributed across many clients, is highly imbalanced, heterogeneous, and subject to strict privacy regulations, the researchers extended CAPS into a federated version called FedCAPS. This framework is designed to integrate feature selection knowledge across multiple clients without requiring them to share their sensitive raw data.
In FedCAPS, instead of sharing raw data, each client collects its own feature selection records (which features were selected and how well they performed on local data) and sends only these records to a central server. The server then uses a permutation-invariant encoder-decoder module, similar to CAPS, to fuse this diverse knowledge into a unified global embedding space. This ensures privacy while still allowing for collaborative learning.
To address the issue of imbalanced and heterogeneous data across clients, FedCAPS incorporates a “sample-aware weighting strategy.” This means that clients with larger datasets contribute more significantly to the global knowledge aggregation, as their data is generally more stable and representative. This helps to reduce bias and ensures that the identified feature subsets generalize well across all participating clients. The policy-guided reinforcement learning agent then explores this unified global embedding space, guided by a weighted average of performance across clients, to find the optimal feature subset.
Also Read:
- LLM-FS-Agent: A Multi-Agent Approach for Clearer Machine Learning Decisions in Feature Selection
- Advancing Automatic Modulation Classification with Federated Self-Supervised Learning
Key Benefits and Findings
Extensive experiments on various datasets demonstrated that both CAPS and FedCAPS consistently outperform existing feature selection and federated learning baselines. The framework’s ability to handle permutation invariance was visually confirmed, showing that permuted feature subsets cluster closely to their original counterparts in the embedding space. The research also highlighted the importance of using “top-K” historical records as initial search seeds for the reinforcement learning agent, leading to faster convergence and more stable performance. Furthermore, the models proved robust across different downstream machine learning tasks (like Random Forest, XGBoost, SVM, KNN, and Decision Tree) and effectively reduced the size of feature subsets while maintaining or improving model performance, showcasing their multi-objective optimization capabilities.
This work represents a significant step forward in automated feature selection, offering robust, efficient, and privacy-preserving solutions for both centralized and distributed data environments. For more technical details, you can refer to the full research paper available at this link.
