Abstract
The imbalanced data classification is one of the most critical challenges in the field of data mining. The state-of-the-art class-overlap under-sampling algorithm considers that the majority nearest neighbors of minority class instances are more prone to class-overlap. When the number of minority instances is small, the instances removed by such methods are not thorough. Therefore, a Sequential Three-way Rules class-overlap undersampling method based on fuzzy hierarchical subspace is proposed, which is inspired by sequential three-way decision. First, the fuzzy hierarchical subspace (FHS) concept is proposed to construct the fuzzy hierarchical subspace structure of the dataset. Then, a sequential three-way rules is constructed to find the equivalent majority instances of the minority instances from the fuzzy hierarchical subspace. We assume that the equivalent majority instances are overlapping instances of the minority class. Finally, in order to preserve the information of the majority instances in the equivalence class, we keep the majority instances with the largest Mahalanobis distance from the center of the equivalence class. Experimental results on 18 real datasets show that S3RCU outperforms or partially outperforms state-of-the-art class-overlap under-sampling methods on two evaluation metrics, F-measure and KAPPA.
This work was supported by the Science Foundation of China.
University of Petroleum, Beijing (No. 2462020YXZZ023).
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Dai, Q., Liu, J.w., Yang, J.p. (2023). Sequential Three-Way Rules Class-Overlap Under-Sampling Based on Fuzzy Hierarchical Subspace for Imbalanced Data. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_2
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