Abstract
Data classification is a commonly used data processing method in the fields of networks and distributed systems, and it has attracted extensive attention in recent years. Nevertheless, the existing classification algorithms are mainly aimed at relatively balanced datasets, while the data in reality often exhibits imbalanced characteristics. In this paper, we propose a novel Hybrid Resampling-based Ensemble (HRE) model, which aims to solve the classification problem of highly skewed data. The main idea of the HRE is to leverage the resampling approach for tackling class imbalance, and then twelve classifiers are further adopted to construct an ensemble model. Besides, a novel combination of under-sampling and over-sampling is elaborately proposed to balance the heterogeneity among different data categories. We decide the resampling rate in an empirical manner, which provides a practical guideline for the use of sampling methods. We compare the effect of different resampling methods based on the imbalanced network anomaly detection dataset, where few abnormal data need to be distinguished from a large number of common network traffics. The results of extensive experiments show that the HRE model achieves better accuracy performance than the methods without hybrid resampling.
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Acknowledgement
The work is supported by the National Key Research and Development Program of China under grant 2018YFB0204301, the National Natural Science Foundation (NSF) under grant 62072306 and 62002378, Tianjin Science and Technology Foundation under Grant No.18ZXJMTG00290, Open Fund of Science and Technology on Parallel and Distributed Processing Laboratory under grant 6142110200407.
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Chen, R., Luo, L., Chen, Y., Xia, J., Guo, D. (2021). A Hybrid Framework for Class-Imbalanced Classification. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_24
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