ISODF-ENN:基于改进扩散模型和ENN的不平衡数据混合采样方法

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhenzhe Lv, Qicheng Liu
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引用次数: 0

摘要

在大数据时代,数据的复杂性越来越高。数据不平衡和类重叠等问题对传统分类器提出了挑战。同时,不平衡数据的重要性日益突出,有必要寻找合适的方法来提高分类器在这类数据集上的分类性能。为此,本文提出了一种基于迭代自组织(ISODATA)去噪扩散算法和编辑近邻(ENN)数据清洗算法的混合采样方法(ISODF-ENN)。该算法首先使用迭代自组织聚类算法将少数类划分到不同的子聚类中,然后使用去噪扩散算法为每个子聚类生成新的少数类数据,最后使用ENN算法对多数类数据进行预处理,去除与少数类数据的重叠。每个子簇按照抽样比例进行过采样,使过采样的少数类数据也符合原始少数类数据的分布。在龙骨数据集上的实验结果表明,该方法在f值和AUC方面优于其他方法,有效地解决了类不平衡和类重叠的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ISODF-ENN:Imbalanced data mixed sampling method based on improved diffusion model and ENN
In the era of big data, the complexity of data is increasing. Problems such as data imbalance and class overlap pose challenges to traditional classifiers. Meanwhile, the importance of imbalanced data has become increasingly prominent, it is necessary to find appropriate methods to enhance classification performance of classifiers on such datasets. In response, this paper proposes a mixed sampling method (ISODF-ENN) based on iterative self-organizing (ISODATA) denoising diffusion algorithm and edited nearest neighbors (ENN) data cleaning algorithm. The algorithm first uses iterative self-organizing clustering algorithm to divide minority class into different sub-clusters, then it uses denoising diffusion algorithm to generate new minority class data for each sub-cluster, and finally it uses ENN algorithm to preprocess majority class data to remove the overlap with the minority class data. Each sub-cluster is oversampled according to sampling ratio, so that the oversampled minority class data also conforms to the distribution of original minority class data. Experimental results on keel datasets demonstrate that the proposed method outperforms other methods in terms of F-value and AUC, effectively addressing the issues of class imbalance and class overlap.
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来源期刊
Journal of Intelligent & Fuzzy Systems
Journal of Intelligent & Fuzzy Systems 工程技术-计算机:人工智能
CiteScore
3.40
自引率
10.00%
发文量
965
审稿时长
5.1 months
期刊介绍: The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
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