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Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization 预览

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摘要 Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.Several techniques have been developed and successfully applied for certain application domains.However,this work demands professional knowledge and expert experience.And sometimes it has to resort to the brute-force search.Therefore,if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method,it will greatly improve the efficiency of machine learning.In this paper,we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes.In this way,the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem.Bayesian optimization is based on the Bayesian theorem.It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function.A utility function selects the next sample point to maximize the optimization function.Several experiments were conducted on standard test datasets.Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models,such as the random forest algorithm and the neural networks,even multi-grained cascade forest under the consideration of time cost.
出处 《电子科技学刊:英文版》 CAS CSCD 2019年第1期26-40,共15页 Journal of Electronic Science Technology
基金 the National Natural Science Foundation of China under Grant No.61503059.
作者简介 Corresponding author:Jia Wu was born in Sichuan Province,China in 1980.She received the M.S.degree in computer science from University of Electronic Science and Technology of China,Chengdu,China in 2006,and the Ph.D.degree in automation from Universitéde Technologie de Belfort-Montbéliard,Belfort,France in 2011.She is currently an associate professor with University of Electronic Science and Technology of China.Her research interests include deep reinforcement learning,data mining,and intelligent transportation systems.e-mail:jiawu@uestc.edu.cn;Xiu-Yun Chen was born in Jiangxi Province,China in 1993.He received the B.S.degree from Jiangxi University of Science and Technology,Jiangxi,China in 2012.He is currently pursuing the M.S.degree with University of Electronic Science and Technology of China.His research interests include deep learning and reinforcement learning.e-mail:996167678@qq.com;Hao Zhang was born in 1993.He received the M.S.degree in computer science from University of Electronic Science and Technology of China in 2018.His research interests include data mining and machine learning.e-mail:857221751@qq.com;Li-Dong Xiong was born in Sichuan Province,China in 1989.He received the B.S.degree from University of Electronic Science and Technology of China in 2008.He is currently pursuing the M.S.degree with University of Electronic Science and Technology of China.His research interests include reinforcement learning and big data processing.e-mail:81912416@qq.com;Hang Lei was born in 1960.He is currently a professor and Ph.D.supervisor with University of Electronic Science and Technology of China.His research interests include embedded system design(hardware and software)and embedded software reliability.e-mail:hlei@uestc.edu.cn;Si-Hao Deng received the Ph.D.degree from Universitéde Technologie de Belfort-Montbéliard in 2007.He is currently an associate professor with Universitéde Technologie de Belfort-Montbéliard.His research interests include automation&robotics and artificial intelligence.e-mail:si
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