Hyper-parameter optimization in classification: To-do or not-to-do
作者：
Highlights：
• We found hyper-param tuning is not well justified in many cases but still very useful in a few.
• We propose a framework to address the problem of deciding to-tune or not-to-tune.
• We implemented a prototype of the framework with 486 datasets and 4 algorithm.
• The results indicates our framework is effective at avoiding effects of ineffective tuning.
• Our framework enables a life-long learning approach to the problem.
摘要
•We found hyper-param tuning is not well justified in many cases but still very useful in a few.•We propose a framework to address the problem of deciding to-tune or not-to-tune.•We implemented a prototype of the framework with 486 datasets and 4 algorithm.•The results indicates our framework is effective at avoiding effects of ineffective tuning.•Our framework enables a life-long learning approach to the problem.
论文关键词：Hyper-parameter optimization,Framework,Bayesian optimization,Machine learning,Incremental learning
论文评审过程：Received 25 June 2019, Revised 19 October 2019, Accepted 25 January 2020, Available online 31 January 2020, Version of Record 25 February 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107245
