| Summary: | Meta learning is an advanced field of Artificial Intelligence (AI) where automatic learning algorithms are applied to acquire learning experience for a set of learning algorithms to improve learning performance. One of popular Meta learning methodologies is based on cross validation, especially for selection processes among different machine learning models. However, the challenge is that it is a very time-consuming to do cross validation among models in large data sets, especially in financial big data with high noise. This paper proposes two Asymptotic Meta learning algorithms (AML-lin and AML-xiang), which are ordinal optimization algorithms for Meta learning based on cross validation. The numerical experiments and real-world cases are conducted to illustrate its efficiency in cross validation of models in different scenarios, especially for financial data. The method proposed in this paper has significant improvement by comparing with those ones in existing algorithms OCBA and IAML (e.g., see reference [8] [9]), and it is new in dealing with financial data.
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