Reinforcement Learning-Based Discriminative Feature Selection for Multivariate Time Series Classification

Multivariate time series classification (MTSC) poses significant challenges in various domains. Existing traditional machine learning (TML) approaches for MTSC offer interpretability, but may include nondiscriminative features, leading to reduced accuracy. Conversely, deep learning (DL) methods achieve high performance but often sacrifice interpretability. To address these limitations, we propose RLDFS, a deep reinforcement learning-based discriminative feature selection framework, designed to identify distinctive features and enhance accuracy. To ensure the reliability of candidate feature sets and mitigate model variance, we leverage an interval-based ensemble method to capture features effectively. Furthermore, we illustrate how features selected through reinforcement learning (RL) can be intuitively interpreted, demonstrating their importance in classification tasks. Extensive experimental evaluations showcase that our framework outperforms state-of-the-art MTSC algorithms.