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<p>Feature selection (FS) is a critical preprocessing step in machine learning to enhance model performance and interpretability. However, the increasing dimensionality of data poses significant challenges for traditional metaheuristic algorithms (MAs), including the novel moss growth optimizer (MGO), which often struggle to balance exploration and exploitation and handle high-dimensional datasets effectively. To address these limitations, we propose QMGO, an MGO enhanced by reinforcement learning (RL) decision-making. QMGO incorporates the random following (RF) strategy to improve exploration and the weighted mean of vectors (INFO) strategy to boost exploitation. A Q-learning mechanism is employed to dynamically select the optimal strategy between RF and INFO during different optimization stages. Furthermore, a binary version, bQMGO, is developed specifically for FS tasks. On the 29 CEC 2017 benchmark functions, QMGO demonstrated exceptional optimization capability, achieving the first rank against both 11 original and 11 advanced algorithms. In feature selection experiments across 14 public datasets, bQMGO achieved zero classification error on all datasets while selecting the minimum number of features. These results confirm that QMGO, by integrating a RL decision framework, is a powerful and reliable tool for feature selection, enriching the application of metaheuristic algorithms in this domain.</p>

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QMGO: A Reinforcement Learning Decision-Making Enhanced Moss Growth Optimizer for Feature Selection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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  • Haogao Song,
  • Lei Liu,
  • Yi Chen,
  • Qian Zhang,
  • Huiling Chen

摘要

Feature selection (FS) is a critical preprocessing step in machine learning to enhance model performance and interpretability. However, the increasing dimensionality of data poses significant challenges for traditional metaheuristic algorithms (MAs), including the novel moss growth optimizer (MGO), which often struggle to balance exploration and exploitation and handle high-dimensional datasets effectively. To address these limitations, we propose QMGO, an MGO enhanced by reinforcement learning (RL) decision-making. QMGO incorporates the random following (RF) strategy to improve exploration and the weighted mean of vectors (INFO) strategy to boost exploitation. A Q-learning mechanism is employed to dynamically select the optimal strategy between RF and INFO during different optimization stages. Furthermore, a binary version, bQMGO, is developed specifically for FS tasks. On the 29 CEC 2017 benchmark functions, QMGO demonstrated exceptional optimization capability, achieving the first rank against both 11 original and 11 advanced algorithms. In feature selection experiments across 14 public datasets, bQMGO achieved zero classification error on all datasets while selecting the minimum number of features. These results confirm that QMGO, by integrating a RL decision framework, is a powerful and reliable tool for feature selection, enriching the application of metaheuristic algorithms in this domain.