A dynamic optimization approach for adaptive incremental learning

A fundamental problem when performing incremental learning is that the best set of a classification system's parameters can change with the evolution of the data. Consequently, unless the system self‐adapts to such changes, it will become obsolete, even if the application environment seems to be static. To address this problem, we propose a dynamic optimization approach in this paper that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses overtime. The approach incorporates various theories, such as dynamic particle swarm optimization, incremental support vector machine classifiers, change detection, and dynamic ensemble selection based on classifiers' confidence levels. Experiments carried out on synthetic and real‐world databases demonstrate that the proposed approach actually outperforms the classification methods often used in incremental learning scenarios. © 2011 Wiley Periodicals, Inc.

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