Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization

By Jia Wu, Xiuyun Chen, H. Zhang, Li-Dong Xiong, Hang Lei, S. Deng. 2019

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Review of Swarm Intelligence-based Feature Selection Methods (2020, methodology): “Machine learning methods utilized in the many areas of medical diagnosis (Liu, Gu et al. 2017, Li, Wu et al. 2019), marketing (Cheng-Lung Huang and Tsai 2009, Yuan, Yuan et al. 2020), image processing (Liang, Zhang et al. 2017, Zhou, Gao et al. 2017), text mining (Wang and Hong 2019, Kou, Yang et…”
Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data (2021, background): “Moreover, it could perform a major role in improving the prediction model because of the direct control of the behaviors of training algorithms [46].”
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization (2023, background): “Hyperparameter Optimization The importance of hyperparameter optimization in machine learning [30, 31, 32, 33, 34, 35], particularly in the context of fine-tuning deep learning language models such as BERT [36] and GPT [37], cannot be ignored.”
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, positive): “As its name suggests, stemming from the Bayesian theorem, the Bayesian optimization is an e ﬃ cient algorithm dealing with such kind of optimization problem [27,35].”