Application Research of Multi-label Learning Under Concept Drift

In order to address the interference of concept drift on the results of multi-label learning algorithms, a hybrid kernel extreme learning machine is used as the foundation for the classification algorithm. Concept drift detection is incorporated, and the classifier is updated based on the detection results for application in multi-label learning. Firstly, the data stream is divided into appropriately sized data blocks, and a hybrid extreme learning machine is used on several of the preceding data blocks to obtain the base classifier. Subsequently, the incoming data blocks are processed using the base classifier to calculate the sample mean and variance between the current data and previous data. Based on this result, it is determined whether concept drift has occurred, and the base classifiers within the ensemble model are retrained and adjusted to update the model.