Hyperparameter Importance Analysis based on N-RReliefF Algorithm


  • Yunlei Sun China University of Petroleum(East China) http://orcid.org/0000-0003-3745-6899
  • Huiquan Gong Faculty of Information Technology Beijing University of Technology, China No.100, Pingleyuan, Chaoyang District, Beijing, 100124, China xinel_ghq@126.com
  • Yucong Li College of Computer & Communication Engineering China University of Petroleum(East China), China No.66, West Changjiang Road, Huangdao District, Qingdao 266580, China
  • Dalin Zhang Beijing Jiaotong University


Hyperparameter optimization, Bayesian optimization, RReliefF Algorithm


Hyperparameter selection has always been the key to machine learning. The Bayesian optimization algorithm has recently achieved great success, but it has certain constraints and limitations in selecting hyperparameters. In response to these constraints and limitations, this paper proposed the N-RReliefF algorithm, which can evaluate the importance of hyperparameters and the importance weights between hyperparameters. The N-RReliefF algorithm estimates the contribution of a single hyperparameter to the performance according to the influence degree of each hyperparameter on the performance and calculates the weight of importance between the hyperparameters according to the improved normalization formula. The N-RReliefF algorithm analyses the hyperparameter configuration and performance set generated by Bayesian optimization, and obtains the important hyperparameters in random forest algorithm and SVM algorithm. The experimental results verify the effectiveness of the N-RReliefF algorithm.


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