Details

Autor(en) / Beteiligte

• Wang, Chunnan
• Wang, Hongzhi
• Xu, Bo
• Song, Xintong
• Shi, Xiangyu
• Bao, Yuhao
• Zheng, Bo
• Mondal, Anirban
• Bhattacharya, Arnab
• Lee Mong Li, Janice
• Mohania, Mukesh
• Goyal, Vikram
• Uday Kiran, Rage
• Reddy, P. Krishna
• Agrawal, Divyakant

Titel

CO-AutoML: An Optimizable Automated Machine Learning System

Ist Teil von

• Database Systems for Advanced Applications, p.509-513

Ort / Verlag

Cham: Springer International Publishing

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniques, but are difficult for users without programming experiences to use, and lack of effective optimization scheme to respond to users’ dissatisfaction with final results. To overcome these defects, we develop CO-AutoML, a user-friendly and optimizable AutoML system. CO-AutoML allows users to interact with the system in a customized mode. Besides, it can continuously optimize the search space of the AutoML technique based on reinforce policy and graph neural network (GNN), and thus provide users with more powerful machine learning schemes. Our system empowers ordinary users to easily and more effectively use AutoML techniques, which has a certain application value and practical significance. Our demonstration video: https://youtu.be/nGnmA7noeJA.