Abstract
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.
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Zhao, Q.L., Jiang, Y.H., Xu, M. (2010). Incremental Learning by Heterogeneous Bagging Ensemble. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_1
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DOI: https://doi.org/10.1007/978-3-642-17313-4_1
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