Construction of the concept drift detection model based on the information entropy of feature distribution and dynamic weighting algorithm

Most of the existing concept drift algorithm focuses on the classification model data streams, some of which overlook the distribution of the feature space and sample space, and the importance of feature selection and weighting. To solve this problem, we propose a dynamic information entropy and feature weighting algorithm based on the distribution of feature items from the dynamic evolution of the concept drift departure. To realize the concept transition, we capture the concept drifting of the data stream by the information entropy, according to the fitness degree between the sample and feature space. We improve the feature dynamic weighting latent dirichlet model, to overcome the problem of the current and historical feature weight assignment, as well as cropping the invalid features. Furthermore, the validity of the proposed algorithm was confirmed by the test in open corpus CCERT and Trec06. ©, 2015, Chinese Institute of Electronics. All right reserved.