Online ensemble learning algorithm for imbalanced data stream

Abstract

In many practical applications, due to the inability to collect complete training data sets at one time, the adaptability of the classifier is poor. Online ensemble learning can better solve this problem. However, most of the data streams are imbalanced. Imbalanced data stream will greatly affect the performance of online ensemble learning algorithm. To reduce the impact of imbalanced data stream, this paper proposes a cost sensitive online ensemble learning algorithm for imbalanced data stream. The algorithm uses a variety of equalization methods, mainly including the construction of initial base-classifier, dynamic calculation of misclassification cost, sampling method of samples in data stream and calculation of weight of base-classifier. Those methods can reduce the influence of imbalanced data stream and improve the classification performance under imbalanced data stream. The experimental results show that the performance of the proposed algorithm has the better classification performance for imbalanced data stream. Finally, the algorithm is applied to the network intrusion detection, and the simulation experiment on NSL-KDD data set can reduce the missing alarm rate and the false alarm rate. The experimental results show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.