Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters

• URL: http://arxiv.org/abs/2003.01751v1
• Date: Tue, 3 Mar 2020 19:26:23 GMT
• Title: Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
• Authors: Bozhou Chen, Kaixin Zhang, Longshen Ou, Chenmin Ba, Hongzhi Wang and Chunnan Wang (Habin Institute of Technology)
• Abstract summary: We propose an efficient automatic parameter optimization approach, which is based on the mapping from data to the corresponding hyper- parameters. We show that the proposed approaches outperform the state-of-the-art apporaches significantly.
• Score: 3.37314595161109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-parameters. Manually optimizing the hyper-parameters is a common method of hyper-parameter tuning. However, it is costly and empirically dependent. Automatic hyper-parameter optimization (autoHPO) is favored due to its effectiveness. However, current autoHPO methods are usually only effective for a certain type of problems, and the time cost is high. In this paper, we propose an efficient automatic parameter optimization approach, which is based on the mapping from data to the corresponding hyper-parameters. To describe such mapping, we propose a sophisticated network structure. To obtain such mapping, we develop effective network constrution algorithms. We also design strategy to optimize the result futher during the application of the mapping. Extensive experimental results demonstrate that the proposed approaches outperform the state-of-the-art apporaches significantly.

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