[Retracted] Review on the Application of Metalearning in Artificial Intelligence

Table 1

The research direction of metalearning in big data.

Research directions	Application scenarios	Research content	Learning objects	Advantages	Disadvantages
Classifier	Data prediction	Decision tree; Logistic regression; Naive Bayes; Neural networks	Classification model with metafeatures	High prediction accuracy	Poor performance in schemes with strong indicator dependence
Metric	Few-shot learning	Relation network [30]; Neural network [30]; Prototypical network [31]; Siamese neural network [32]	Metric space	Learning in space is efficient	Not applicable to regression and reinforcement learning
Optimizer	Finding the best strategy	Matching network [33]; Graph neural network [34]; Gradient descent [36]; Curvature information matrix [37]	Optimizer	Metalearner can independently design an optimizer to complete new tasks	High optimization cost