Adaptive Classification Algorithm for Concept Drift Data Stream

Data stream classification is one of the most important tasks in data mining. The performance of a model classifier degrades due to concept drift even in stationary data; dealing with this problem hence becomes more challenging in data streams. The extreme learning machine is widely used in data stream classification. However, the parameters of the extreme learning machine have to be determined in advance. It is not applicable for data stream classification since the fixed parameters cannot adapt a change in the concept or distribution of dataset over the time. To tackle this problem, this paper proposes an adaptive online sequential extreme learning machine algorithm. It outperforms the existing approaches in terms of classification results and adaptability of concept drift. It has an adjustable mechanism for model complexity so that the performance of the classification is improved. The proposed extreme learning machine is robust for the concept drift via adaptive learning based on a forgetting factor and the concept drift detection. In addition, the proposed algorithm is able to detect anomalies to prevent classification decision boundaries from being ruined. Extensive experiments demonstrate that the proposed approach outperforms competitors in terms of stability, classification accuracy, and adaptive ability. Moverover, the effectiveness of the proposed mechanisms has been approved via ablation experiments.