面向电动汽车充电基础设施异常检测的机器学习模型的基于剪枝的TinyML优化
Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
AI总结：
针对电动汽车充电基础设施资源受限场景下的异常检测需求，提出结合Optuna超参调优、SHAP特征选择与非结构化剪枝的TinyML优化方法，在CICEVSE2024数据集上验证了可大幅缩减MLP、LSTM、XGBoost模型的大小与推理时间，且性能损失极小。
摘要：
随着物联网设备对实时处理的需求日益增长，优化机器学习（ML）模型的大小、延迟和计算效率至关重要。本文研究了一种适用于资源受限环境下异常检测的剪枝方法，专门针对电动汽车充电基础设施（EVCI）场景。我们使用CICEVSE2024数据集，通过Optuna进行超参数调优，训练并优化了多层感知器（MLP）、长短期记忆网络（LSTM）和XGBoost三个模型，再利用基于SHapley Additive exPlanations（SHAP）的特征选择（FS）和非结构化剪枝技术对其进一步细化。优化后的模型在模型大小和推理时间上实现了显著缩减，仅对性能产生边际影响。值得注意的是，我们的研究结果表明，在EVCI场景下，剪枝和特征选择可以提升计算效率，同时保留关键的异常检测能力。
英文摘要：
With the growing need for real-time processing on IoT devices, optimizing machine learning (ML) models' size, latency, and computational efficiency is essential. This paper investigates a pruning method for anomaly detection in resource-constrained environments, specifically targeting Electric Vehicle Charging Infrastructure (EVCI). Using the CICEVSE2024 dataset, we trained and optimized three models-Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost-through hyperparameter tuning with Optuna, further refining them using SHapley Additive exPlanations (SHAP)-based feature selection (FS) and unstructured pruning techniques. The optimized models achieved significant reductions in model size and inference times, with only a marginal impact on their performance. Notably, our findings indicate that, in the context of EVCI, pruning and FS can enhance computational efficiency while retaining critical anomaly detection capabilities.
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。
