Method for constructing target prediction model in multicenter small sample scenario and prediction method

Abstract

The invention provides a method for constructing a target prediction model in a multicenter small sample scenario and a prediction method. In combination with the idea of transfer learning, a training set of a new node is predicted by directly using knowledge of a trained node, and a prediction error sample is used to reflect a difference between the new node and the trained node. The difference is used as supplementary knowledge. Model knowledge of the new node is quickly acquired, to avoid training of a new node from scratch each time. Finally, parallel integration of incremental subclassifiers is implemented by using a ridge regression method, so that the deployment time and costs are greatly reduced. The generalization of models is ensured through sharing of historical knowledge and a knowledge discarding mechanism, good classification effect can also be achieved for a node with a small sample size.