Handling Concept Drifts Using Dynamic Selection of Classifiers

This work describes the Dynse framework, which uses dynamic selection of classifiers to deal with concept drift. Basically, classifiers trained on new supervised batches available over time are add to a pool, from which is elected a custom ensemble for each test instance during the classification time. The Dynse framework is highly customizable, and can be adapted to use any method for dynamic selection of classifiers given a test instance. In this work we propose a default configuration for the framework which has provided promising results in a range of problems. The experimental results have shown that the proposed framework achieved the best average rank when considering all datasets, and outperformed the state-of-the-art in three of four tested datasets.