Abstract
In practical applications, many data in imbalanced data streams are unlabeled. For this reason, this paper presents an ensemble classification algorithm for imbalanced data streams with unlabeled data . The algorithm uses the K-means clustering algorithm to label unlabeled instances, uses the re-sampling technique of dividing negative instances to establish some balanced data subsets, uses the Hoeffding Bounds inequality to detect concept drifts, and uses the ensemble technique to build an ensemble classifier. Experimental results show that the algorithm has good classification accuracy and adaptability, and it is an effective classification algorithm.
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Acknowledgment
The work was supported by the National Natural Science Foundation of China (61906044), and supported by Fuyang Government and Fuyang Normal University Cooperation Research Project (XDHX2016024), and supported by Big Data and Intelligent Computing Innovation Team of Fuyang Normal University (XDHXTD201703), and supported by Fuyang Humanities and Social Science Research Special Project (FYSK2019QD10), and supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China (KJ2018A0328, KJ2019A0532 and KJ2019A0542).
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Wang, Z., Sun, G., Zhao, J., Wang, H., Ding, Z. (2021). An Ensemble Classification Algorithm for Imbalanced Data Streams with Unlabeled Data. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_130
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DOI: https://doi.org/10.1007/978-3-030-70665-4_130
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