政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142011
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    题名: A virtual multi-label approach to imbalanced data classification
    作者: 周珮婷
    Chou, Elizabeth P.
    Yang, Shan-Ping
    贡献者: 統計系
    关键词: Imbalance;Classification;Virtual multi-label;Equal k-means
    日期: 2022-03
    上传时间: 2022-09-21 11:44:38 (UTC+8)
    摘要: One of the most challenging issues in machine learning is imbalanced data analysis. Usually, in this type of research, correctly predicting minority labels is more critical than correctly predicting majority labels. However, traditional machine learning techniques easily lead to learning bias. Traditional classifiers tend to place all subjects in the majority group, resulting in biased predictions. Machine learning studies are typically conducted from one of two perspectives: a data-based perspective or a model-based perspective. Oversampling and undersampling are examples of data-based approaches, while the addition of costs, penalties, or weights to optimize the algorithm is typical of a model-based approach. Some ensemble methods have been studied recently. These methods cause various problems, such as overfitting, the omission of some information, and long computation times. In addition, these methods do not apply to all kinds of datasets. Based on this problem, the virtual labels (ViLa) approach for the majority label is proposed to solve the imbalanced problem. A new multiclass classification approach with the equal K-means clustering method is demonstrated in the study. The proposed method is compared with commonly used imbalance problem methods, such as sampling methods (oversampling, undersampling, and SMOTE) and classifier methods (SVM and one-class SVM). The results show that the proposed method performs better when the degree of data imbalance increases and will gradually outperform other methods.
    關聯: Communications in Statistics - Simulation and Computation
    数据类型: article
    DOI 連結: https://doi.org/10.1080/03610918.2022.2049820
    DOI: 10.1080/03610918.2022.2049820
    显示于类别:[統計學系] 期刊論文

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