AD-NEV：一种用于多变量异常检测的可扩展多层神经进化框架
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection
AI总结：
AD-NEV提出一种可扩展多层神经进化框架，通过协同优化特征子空间、模型架构和网络权重，在多变量时间序列异常检测中超越主流深度学习模型并具备高可扩展性。
摘要：
异常检测工具和方法在现代信息物理系统与故障预测系统中具有关键能力。尽管用于异常检测的深度学习架构发展迅速，但针对给定数据集的模型优化仍然是一个繁琐且耗时的过程。神经进化可以成为该问题的有效且高效的解决方案，因为它是一种全自动搜索方法，用于学习最优神经网络，同时支持基于梯度和非梯度的微调。然而，现有方法大多只关注优化模型架构，而没有考虑特征子空间和模型权重。在本工作中，我们提出了异常检测神经进化（AD-NEv）——一个用于多变量时间序列异常检测的可扩展多层优化神经进化框架。该方法代表了一种新颖的协同方式：i）基于装袋技术为集成模型优化特征子空间；ii）优化单个异常检测模型的模型架构；iii）对网络权重进行非梯度微调。在广泛采用的多变量异常检测基准数据集上进行的大量实验评估表明，AD-NEv 提取的模型优于知名的深度学习异常检测架构。此外，结果表明 AD-NEv 能够高效地完成整个过程，在多个 GPU 可用时表现出高可扩展性。
英文摘要：
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and non-gradient fine tuning. However, existing methods mostly focus on optimizing model architectures without taking into account feature subspaces and model weights. In this work, we propose Anomaly Detection Neuroevolution (AD-NEv) - a scalable multi-level optimized neuroevolution framework for multivariate time series anomaly detection. The method represents a novel approach to synergically: i) optimize feature subspaces for an ensemble model based on the bagging technique; ii) optimize the model architecture of single anomaly detection models; iii) perform non-gradient fine-tuning of network weights. An extensive experimental evaluation on widely adopted multivariate anomaly detection benchmark datasets shows that the models extracted by AD-NEv outperform well-known deep learning architectures for anomaly detection. Moreover, results show that AD-NEv can perform the whole process efficiently, presenting high scalability when multiple GPUs are available.
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