Data Stream Ensemble Classification Algorithm Based on Information Entropy Updating Weight

In the dynamic data stream,due to its instability and the existence of concept drift,the ensemble classification model needs the ability to adapt to the new environment in time.At present,the weight of the base classifier is usually updated by using the supervision information,so as to give higher weight to the base classifier suitable for the current environment.However,supervision information cannot be obtained immediately in a real data stream environment.In order to solve this problem,this paper presents a data stream ensemble classification algorithm,which updates the weight of the base classifier through information entropy.Firstly,the random feature subspace is used to initialize each base classifier to construct the ensemble classifier.Secondly,a new base classifier is constructed based on each new data block to replace the base classifier with the lowest weight in the ensemble.Then,the weight update strategy based on information entropy will update the weights in the base classifier in real time.Finally,the base classifier that meets the requirements participates in weighted voting to obtain the classification result.Comparing the proposed algorithm with several other classic learning algorithms,the experimental results show that the proposed me-thod has obvious advantages in classification accuracy and is suitable for various types of concept drift environments.