Dynamic Weighting Ensembles for Incremental Learning

This paper investigates an interesting question of solving incremental learning problems using ensemble algorithms. The motivation is to help classifiers learn additional information from new batches of data incrementally while preserving previously acquired knowledge. Experimental results show that the proposed dynamic weighting scheme can achieve better performance compared to the fixed weighting scheme on a variety of standard UCI benchmark datasets.