Survey on Meta-Learning Research of Algorithm Selection.

With the rapid development of artificial intelligence, the selection of algorithms that meet application requirements from feasible algorithms has become a critical problem to be solved urgently in various fields, that is, the algorithm selection problem. The approach based on meta-learning is an important way to solve the algorithm selection problem, which is widely applied in algorithm selection research and achieves good results. The approach selects appropriate algorithms by constructing the mapping model from problem features to candidate algorithms performance, mainly including the steps of extracting meta-features, calculating candidate algorithms performance, constructing meta-dataset and training meta-model, etc. Firstly, this paper expounds the concept and framework of algorithm selection based on meta-learning, and reviews related surveys. Secondly, it summarizes the research progress from three aspects: meta-features, meta-learners and meta-model performance measures, introduces typical methods and compares the advantages, disadvantages and application scope of different types of methods. Then, it outlines the application of algorithm selection based on meta-learning in different learning tasks. Next, it utilizes 140 classification datasets, 9 candidate classification algorithms and 5 performance indicators to conduct algorithm selection experiments to compare the performance of different algorithm selection methods. Finally, it analyzes the current challenges and problems, and discusses future development directions. [ABSTRACT FROM AUTHOR]