Imbalanced Data Over-Sampling Method Based on ISODATA Clustering-Reference-Cited by-同舟云学术

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Imbalanced Data Over-Sampling Method Based on ISODATA Clustering

Published:2023-09-01 Issue:9 Volume:E106.D Page:1528-1536
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

LV Zhenzhe¹,LIU Qicheng¹

Affiliation:

1. School of Computer and Control Engineering, Yantai University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E106.D/9/E106.D_2022EDP7190/_pdf

Reference33 articles.

1. [1] V.N. Dornadula, S. Geetha, “Credit card fraud detection using machine learning algorithms,” Procedia computer science, vol.165, pp.631-641, 2019. doi: 10.1016/j.procs.2020.01.057 10.1016/j.procs.2020.01.057

2. [2] K. Burghardt, Z. Maoz, and E. Estrada, “Dyadic imbalance in networks,” Journal of Complex Networks, vol.8, pp.cnaa001, 2020. doi: 10.1093/comnet/cnaa001 10.1093/comnet/cnaa001

3. [3] Z. Wang, L. Li, J. Yan, and Y. Yao, “Approaching high-accuracy side effect prediction of traditional Chinese medicine compound prescription using network embedding and deep learning,” IEEE Access, vol.8, pp.82493-82499, 2020. doi:10.1109/ACCESS.2020.2991750 10.1109/access.2020.2991750

4. [4] S. Rao, A.K. Verma, and T. Bhatia, “Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data,” Expert Systems with Applications, vol.217, 119594, 2023. doi:10.1016/J.ESWA.2023.119594 10.1016/j.eswa.2023.119594

5. [5] Y. Zheng, G. Li, and W. Zhang, “A New Efficient Algorithm Based on Multi-Classifiers Model for Classification,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.28, pp.25-46, 2020. doi: 10.1142/S0218488520500026 10.1142/s0218488520500026

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