提出自适应随机森林与可解释性方法，让网络AI持续学习新特征并高效推理。
Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
- 用迭代训练的自适应随机森林动态适应网络新特征
- 加入更多特征后准确率持续提升，运行时间最多降低50%
- 通过漂移检测智能触发解释方法，适合6G网络运维人员
随着AI成为6G网络控制的原生组件，模型需适应不断变化的环境，包括多厂商部署带来的新特征、硬件升级和演进的服务需求。本文提出自适应随机森林（ARFs）作为通信网络中动态特征适配的可靠方案。实验表明，对ARFs进行迭代训练可实现稳定预测，且随着新特征引入，准确率持续提升。同时强调AI可解释性的重要性，提出基于分布漂移检测的轻量级特征重要性方法（DAFI），仅在检测到显著漂移时才启用计算密集型解释方法。在3个不同数据集上的测试显示，该方法将运行时间最多降低2倍，同时获得更一致的特征重要性结果。ARFs与DAFI共同构成面向6G网络用例的灵活、可解释的AI框架。
原文摘要 · Abstract (English)
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven by multi-vendor deployments, hardware upgrades, and evolving service requirements. To address this growing need for flexible learning in non-stationary environments, this vision paper highlights Adaptive Random Forests (ARFs) as a reliable solution for dynamic feature adaptation in communication network scenarios. We show that iterative training of ARFs can effectively lead to stable predictions, with accuracy improving over time as more features are added. In addition, we highlight the importance of explainability in AI-driven networks, proposing Drift-Aware Feature Importance (DAFI) as an efficient XAI feature importance (FI) method. DAFI uses a distributional drift detector to signal when to apply computationally intensive FI methods instead of lighter alternatives. Our tests on 3 different datasets indicate that our approach reduces runtime by up to 2 times, while producing more consistent feature importance values. Together, ARFs and DAFI provide a promising framework to build flexible AI methods adapted to 6G network use-cases.
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