Hyperparameter Importance Analysis based on N-RReliefF Algorithm
作者: Yunlei SunHuiquan GongYucong LiDalin Zhang
刊名: International Journal of Computers Communications & Control, 2019, Vol.14 (4), pp.557-573
来源数据库: Agora University
DOI: 10.15837/ijccc.2019.4.3593
关键词: Hyperparameter optimizationBayesian optimizationRReliefF Algorithm
原始语种摘要: 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...
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  • optimization 最佳化
  • algorithm 算法
  • weight 
  • normalization 正规化
  • effectiveness 有效性
  • importance 重要性
  • Analysis 分析
  • machine 机器
  • performance 性能
  • constraints 系统规定参数