An Ensemble of Classifiers Algorithm Based on GA for Handling Concept-Drifting Data Streams

In data streams, concepts are often not stable but change with time. In this paper, we propose a selective integration algorithm DGASEN (Dynamic GA based Selected ENsemble) for handling concept-drifting data streams. This algorithm selects a near optimal subset of base classifiers based on GA algorithm and the predictive accuracy of each base classifier on validation dataset. This paper chooses SEA(with simulating abrupt concept drift) and Hyperplane (with gradual concept drift) as experimental data sets. The experimental results demonstrate that selective integration of classifiers can be significantly better than majority voting and weighted voting, which are currently the most commonly used integration techniques for handling concept drift in ensemble learning. The experimental results show that DGASEN algorithm improves the classification accuracy of integrated algorithm in handling concept-drifting data streams.