Abstract
Learning from imbalanced data streams is one of the challenges for classification algorithms and learning classifiers. The goal of the paper is to propose and validate a new approach for learning from data streams. However, the paper references a problem of class-imbalanced data. In this paper, a hybrid approach for changing the class distribution towards a more balanced data using the over-sampling and instance selection techniques is discussed. The proposed approach assumes that classifiers are induced from incoming blocks of instances, called data chunks. These data chunks consist of incoming instances from different classes and a balance between them is obtained through the hybrid approach. These data chunks are next used to induce classifier ensembles. The proposed approach is validated experimentally using several selected benchmark datasets and the computational experiment results are presented and discussed. The results of the computational experiment show that the proposed approach for eliminating class imbalance in data streams can help increase the performance of online learning algorithms.
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Notes
- 1.
The best solution obtained by the compared algorithms is indicated in bold. The underline indicates the best solution obtained by the WECOI or WECU algorithm.
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Czarnowski, I. (2021). Learning from Imbalanced Data Streams Based on Over-Sampling and Instance Selection. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_32
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