带有可解释Kolmogorov-Arnold网络的双深度Q网络的人机交互特征选择
Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network
AI总结：
本文提出一种基于Kolmogorov-Arnold网络的双深度Q网络，实现人机交互的特征选择，提升模型准确性和可解释性，同时在多个数据集上验证了其高效性和扩展性。
摘要：
特征选择对于提高机器学习模型的性能和可解释性至关重要，特别是在高维空间中，复杂的特征交互可能会降低准确性和增加计算需求。现有方法通常依赖于静态特征子集或人工干预，限制了适应性和扩展性。然而，动态、按实例特征选择方法和强化学习中的模型特定可解释性仍处于探索阶段。本研究提出了一种集成了双深度Q网络（DDQN）的带有人机交互（HITL）特征选择框架，使用Kolmogorov-Arnold网络（KAN）。我们的新方法利用模拟人类反馈和基于随机分布的采样，特别是Beta分布，以迭代方式根据数据实例优化特征子集，提高特征选择的灵活性。KAN-DDQN在MNIST上实现了93%的测试准确率，在FashionMNIST上实现了83%的测试准确率，比传统MLP-DDQN模型高出高达9%。基于KAN的模型通过符号表示提供了高可解释性，同时在隐藏层中使用了比MLP少四倍的神经元。相比之下，没有特征选择的模型在MNIST上仅实现了58%的测试准确率，在FashionMNIST上仅实现了64%的测试准确率，突显了我们框架的显著优势。我们进一步在CIFAR-10和CIFAR-100上验证了扩展性，实现了在MNIST上的高达30%的相对宏F1改进，在CIFAR-10上实现了5%的改进，同时将校准误差降低了25%。复杂性分析证实了实时可行性，延迟低于1毫秒，参数数量低于0.02M。剪枝和可视化进一步增强了模型的透明度，通过阐明决策路径。这些发现呈现了一种可扩展、可解释的特征选择解决方案，适用于需要实时、自适应决策且最小人类监督的应用。
英文摘要：
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. We further validate scalability on CIFAR-10 and CIFAR-100, achieving up to 30% relative macro F1 improvement on MNIST and 5% on CIFAR-10, while reducing calibration error by 25%. Complexity analysis confirms real-time feasibility with latency below 1 ms and parameter counts under 0.02M. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
