Ensemble diversity enhancement based on parameters evolution of base learners

Ensemble learning shows very good performance when dealing with the problem of concept drift. Accuracy and diversity are two important characteristics of ensemble learning, and diversity is the key factor affecting the generalization ability of ensemble learning. In this paper, a modeling method is proposed to enhance diversity among base learners based on parameters evolution of base learners. The method uses online sequential extreme learning machine (OS_ELM) as base learner. The base learners are grouped according to their performance on the sliding window, and perform evolution operations. At the same time, the concept of gene flow is introduced, which increases the diversity among base learners and improves the prediction performance of the ensemble algorithm in dealing with the concept drift data streams. Finally, the rationality and effectiveness of the proposed algorithm are verified by using the synthetic data sets and real-world data sets.