GWO Optimized K-Means Cluster based Oversampling Algorithm

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Published Aug 3, 2021
https://doi.org/10.47164/ijngc.v12i3.694
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Volume 12, Issue 3, July 2021

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Santha Subbulaxmi S
RESEARCH SCHOLAR, MADURAI KAMARAJ UNIVERSITY, MADURAI, TAMIL NADU, INDIA.
Arumugam G

Abstract

Skewed data distribution prevails in many real world applications. The skewedness is due to imbalance in the class distribution and it deteriorates the performance of the traditional classification algorithms. In this paper, we provide a Grey wolf optimized K-Means cluster based oversampling algorithm to handle the skewedness and solve the imbalanced data classification problem. Experiments are conducted on the proposed algorithm and compared it with the benchmarking popular algorithms. The results reveal that the proposed algorithm outperforms the other benchmarking algorithms.

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
S, S. S., & G, A. (2021). GWO Optimized K-Means Cluster based Oversampling Algorithm. International Journal of Next-Generation Computing, 12(3), 343–355. https://doi.org/10.47164/ijngc.v12i3.694
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