Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interactions can reduce accuracy and increase computational demands. Existing approaches often rely on static feature subsets or manual intervention, limiting adaptability and scalability. However, dynamic, per-instance feature selection methods and model-specific interpretability in reinforcement learning remain underexplored. This study proposes a human-in-the-loop (HITL) feature selection framework integrated into a Double Deep Q-Network (DDQN) using a Kolmogorov-Arnold Network (KAN). Our novel approach leverages simulated human feedback and stochastic distribution-based sampling, specifically Beta, to iteratively refine feature subsets per data instance, improving flexibility in feature selection. The KAN-DDQN achieved notable test accuracies of 93% on MNIST and 83% on FashionMNIST, outperforming conventional MLP-DDQN models by up to 9%. The KAN-based model provided high interpretability via symbolic representation while using 4 times fewer neurons in the hidden layer than MLPs did. Comparatively, the models without feature selection achieved test accuracies of only 58% on MNIST and 64% on FashionMNIST, highlighting significant gains with our framework. Pruning and visualization further enhanced model transparency by elucidating decision pathways. These findings present a scalable, interpretable solution for feature selection that is suitable for applications requiring real-time, adaptive decision-making with minimal human oversight.

翻译：特征选择对于提升机器学习模型的性能与可解释性至关重要，尤其在复杂特征交互可能降低精度并增加计算需求的高维空间中。现有方法通常依赖静态特征子集或人工干预，限制了适应性与可扩展性。然而，动态的实例级特征选择方法及强化学习中模型特定的可解释性研究仍显不足。本研究提出一种集成于双深度Q网络（DDQN）的人机协同（HITL）特征选择框架，该框架采用Kolmogorov-Arnold网络（KAN）。我们的创新方法利用模拟人类反馈和基于随机分布（特别是Beta分布）的采样，针对每个数据实例迭代优化特征子集，从而提升特征选择的灵活性。KAN-DDQN在MNIST和FashionMNIST数据集上分别取得了93%和83%的显著测试准确率，较传统MLP-DDQN模型提升高达9%。基于KAN的模型通过符号表示提供高可解释性，且隐藏层神经元数量仅为多层感知机的四分之一。相比之下，未进行特征选择的模型在MNIST和FashionMNIST上的测试准确率分别仅为58%和64%，凸显了本框架带来的显著性能提升。剪枝与可视化技术通过阐明决策路径进一步增强了模型透明度。这些研究成果为特征选择提供了一种可扩展、可解释的解决方案，适用于需要实时自适应决策且人工监督最少的应用场景。