Classifier ensemble is a main direction of incremental learning researches, and many ensemble-based incremental learning methods have been presented. Among them, Learn++, which is derived from the famous ensemble algorithm, AdaBoost, is special. Learn++ can work with any type of classifiers, either they are specially designed for incremental learning or not, this makes Learn++ potentially supports heterogeneous base classifiers. Based on massive experiments we analyze the advantages and disadvantages of Learn++. Then a new ensemble incremental learning method, Bagging++, is presented, which is based on another famous ensemble method: Bagging. The experimental results show that Bagging ensemble is a promising method for incremental learning and heterogeneous Bagging++ has the better generalization and learning speed than other compared methods such as Learn++ and NCL. © 2010 Springer-Verlag.
CITATION STYLE
Zhao, Q. L., Jiang, Y. H., & Xu, M. (2010). Incremental learning by heterogeneous Bagging ensemble. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6441 LNAI, pp. 1–12). https://doi.org/10.1007/978-3-642-17313-4_1
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