An improved SVM-KM model for imbalanced datasets

Weiguo Deng; Li Wang; Yiyang Wang; Zhong Qian

doi:10.1109/ICICEE.2012.35

An improved SVM-KM model for imbalanced datasets

Weiguo Deng^*
, Li Wang
, Yiyang Wang
, Zhong Qian

^*此作品的通讯作者

经济管理学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Support vector machine is a widely used machine learning technique. SVM-KM model can speed SVM training by eliminating non support vectors, but imbalanced datasets will affect the classification accuracy. In this paper, we proposed an improved SVM-KM model, which assign different error costs to different classes. Based on the simulation results, the improved SVM-KM model performed best for imbalanced datasets.

源语言	英语
主期刊名	Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012
页	100-103
页数	4
DOI	https://doi.org/10.1109/ICICEE.2012.35
出版状态	已出版 - 2012
活动	2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012 - Xi'an, 中国期限: 23 8月 2012 → 25 8月 2012

出版系列

姓名	Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012

会议

会议	2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012
国家/地区	中国
市	Xi'an
时期	23/08/12 → 25/08/12

访问文件

10.1109/ICICEE.2012.35

其它文件与链接

链接到 Scopus 的出版物

引用此

Deng, W., Wang, L., Wang, Y., & Qian, Z. (2012). An improved SVM-KM model for imbalanced datasets. 在 Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012 (页码 100-103). 文章 6322324 (Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012). https://doi.org/10.1109/ICICEE.2012.35

@inproceedings{08ca0831c18c4d92a50cc970476d5aac,

title = "An improved SVM-KM model for imbalanced datasets",

abstract = "Support vector machine is a widely used machine learning technique. SVM-KM model can speed SVM training by eliminating non support vectors, but imbalanced datasets will affect the classification accuracy. In this paper, we proposed an improved SVM-KM model, which assign different error costs to different classes. Based on the simulation results, the improved SVM-KM model performed best for imbalanced datasets.",

keywords = "different error costs, imbalanced datasets, k-means, support vector machine",

author = "Weiguo Deng and Li Wang and Yiyang Wang and Zhong Qian",

year = "2012",

doi = "10.1109/ICICEE.2012.35",

language = "英语",

isbn = "9780769547923",

series = "Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012",

pages = "100--103",

booktitle = "Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012",

note = "2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012 ; Conference date: 23-08-2012 Through 25-08-2012",

}

Deng, W, Wang, L, Wang, Y & Qian, Z 2012, An improved SVM-KM model for imbalanced datasets. 在 Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012., 6322324, Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, 页码 100-103, 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, Xi'an, 中国, 23/08/12. https://doi.org/10.1109/ICICEE.2012.35

An improved SVM-KM model for imbalanced datasets. / Deng, Weiguo; Wang, Li; Wang, Yiyang 等.
Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012. 2012. 页码 100-103 6322324 (Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - An improved SVM-KM model for imbalanced datasets

AU - Deng, Weiguo

AU - Wang, Li

AU - Wang, Yiyang

AU - Qian, Zhong

PY - 2012

Y1 - 2012

N2 - Support vector machine is a widely used machine learning technique. SVM-KM model can speed SVM training by eliminating non support vectors, but imbalanced datasets will affect the classification accuracy. In this paper, we proposed an improved SVM-KM model, which assign different error costs to different classes. Based on the simulation results, the improved SVM-KM model performed best for imbalanced datasets.

AB - Support vector machine is a widely used machine learning technique. SVM-KM model can speed SVM training by eliminating non support vectors, but imbalanced datasets will affect the classification accuracy. In this paper, we proposed an improved SVM-KM model, which assign different error costs to different classes. Based on the simulation results, the improved SVM-KM model performed best for imbalanced datasets.

KW - different error costs

KW - imbalanced datasets

KW - k-means

KW - support vector machine

UR - https://www.scopus.com/pages/publications/84869992865

U2 - 10.1109/ICICEE.2012.35

DO - 10.1109/ICICEE.2012.35

M3 - 会议稿件

AN - SCOPUS:84869992865

SN - 9780769547923

T3 - Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012

SP - 100

EP - 103

BT - Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012

T2 - 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012

Y2 - 23 August 2012 through 25 August 2012

ER -

Deng W, Wang L, Wang Y, Qian Z. An improved SVM-KM model for imbalanced datasets. 在 Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012. 2012. 页码 100-103. 6322324. (Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012). doi: 10.1109/ICICEE.2012.35

An improved SVM-KM model for imbalanced datasets

摘要

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会议

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引用此