An oversampling FCM-KSMOTE algorithm for imbalanced data classification

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-12-01 DOI:10.1016/j.jksuci.2024.102248
Hongfang Zhou , Jiahao Tong , Yuhan Liu , Kangyun Zheng , Chenhui Cao
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Abstract

In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose a novel oversampling method named FCM-KSMOTE. The algorithm initially performs a density-based fuzzy clustering on the data, then iterates to partition regions and perform oversampling inside each cluster. Secondly, it merges the clusters and conducts noise detection to obtain a balanced dataset. Finally, we conducted the experiments on 19 public datasets and 3 synthetic datasets. Six evaluation metrics of Recall, Accuracy, G-mean, Specificity, AUC and F1-Score were used in the experiments. The experimental results demonstrate that our method can significantly improve the recognition rate of the minority class while maintaining high accuracy for the majority class. Particularly with the RF classifier, our method ranks first in all evaluation metrics, with a Recall difference of up to 0.2 compared to the least performing method, demonstrating its substantial performance advantage.
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近年来,不平衡数据分类已成为一项具有挑战性的任务。针对这一问题,我们提出了一种名为 FCM-KSMOTE 的新型超采样方法。该算法首先对数据进行基于密度的模糊聚类,然后迭代划分区域,并在每个聚类内部进行超采样。其次,该算法合并聚类并进行噪声检测,以获得平衡的数据集。最后,我们在 19 个公共数据集和 3 个合成数据集上进行了实验。实验中使用了 Recall、Accuracy、G-mean、Specificity、AUC 和 F1-Score 六个评价指标。实验结果表明,我们的方法可以显著提高少数人类别的识别率,同时保持多数人类别的高准确率。特别是在 RF 分类器方面,我们的方法在所有评价指标中都名列第一,与表现最差的方法相比,我们的方法的 Recall 差值高达 0.2,显示了其巨大的性能优势。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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