With the development of booming AutoML systems, modeling processes have become more automatic for researchers. However, AutoML systems may struggle to identify the optimal surrogate type, find the best combination of the hyper-parameters or establish a high-fidelity ensembled surrogate model for certain datasets. To address these issues and further improve the warm-start procedure of AutoML, a Ranking Prediction Strategy assisted Automatic Model Selection (RPS-AMS) method is proposed. In the suggested method, an integration of evolutionary algorithms (EA-based) and feature-based driven model s...
