ExperienceThinking: Constrained hyperparameter optimization based on knowledge and pruning

Machine learning models are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize hyperparameter settings according to known evaluation information, so as to effectively promote the performance of the machine learning models within the limited and small budget. Motivated by this, in this paper, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations. ExperienceThinking designs two novel approaches, which make full use of the known evaluation information to intelligently infer optimal configurations from two aspects: search space pruning and knowledge utilization respectively. Two approaches suit for two different kinds of constrained hyperparameter optimization problems, they complement with each other and their combination increases the generality and effectiveness of the ExperienceThinking. To demonstrate the benefit of ExperienceThinking, we conduct extensive experiments using various constrained hyperparameter optimization problems, and compare it with classic hyperparameter optimization algorithms. The experimental results present that our proposed algorithm provides superior results and the design of our proposed algorithm is reasonable.