EAT-ML: Efficient Automatic Tuning for Machine Learning Models in Cyber Physical System

Abstract

In cyber physical system, the machine learning model is a competitive tool and has been successfully applied in this field. However, applying machine learning models to specific problems requires highly experienced manual tuning, which is a process of constant trial and error. Therefore, it often takes huge resources and time when faced with complex problems. In this paper, we propose an efficient method EAT-ML for automatically tuning machine learning models in cyber physical system. This method can automatically tune the hyperparameters of the machine learning model by using a controller with little human intervention. Specifically, the controller sequentially selects the hyperparameters of the machine learning model, and then uses the accuracy obtained on the verification set as the reward value signal. Finally, the PPO algorithm is used to calculate the loss function to update the internal parameters of the controller. In order to further improve the efficiency of the tuning method, we use the previous optimization experience by reconstructing the advantage function. In the experiment, our proposed method can show the best performance on most tasks by comparing other automatic tuning methods. In addition, the effectiveness and feasibility of the various components of the proposed method are also verified through ablation experiments.