A learning approach with under-and over-sampling for imbalanced data sets

Chun Wu Yeh, Der Chiang Li, Liang Sian Lin, Tung I. Tsai

研究成果: Conference contribution

4 引文 (Scopus)

摘要

It is difficult for learning models to achieve high classification performance with imbalanced data sets. To conquer the problem, this study presents a strategy involving the reduction of size of majority data set and the generation of synthetic samples of minority data set. Parkinson's disease data set is used to examine and to compare the performance of classification methods. The paired t-tests are also used to show the effectiveness of the proposed method comparing with that of the other methods.

原文English
主出版物標題Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
編輯Ayako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
發行者Institute of Electrical and Electronics Engineers Inc.
頁面725-729
頁數5
ISBN(電子)9781467389853
DOIs
出版狀態Published - 2016 八月 31
事件5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
持續時間: 2016 七月 102016 七月 14

出版系列

名字Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
國家Japan
城市Kumamoto
期間16-07-1016-07-14

指紋

Sampling

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

引用此文

Yeh, C. W., Li, D. C., Lin, L. S., & Tsai, T. I. (2016). A learning approach with under-and over-sampling for imbalanced data sets. 於 A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (編輯), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (頁 725-729). [7557706] (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.20
Yeh, Chun Wu ; Li, Der Chiang ; Lin, Liang Sian ; Tsai, Tung I. / A learning approach with under-and over-sampling for imbalanced data sets. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編輯 / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. 頁 725-729 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).
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title = "A learning approach with under-and over-sampling for imbalanced data sets",
abstract = "It is difficult for learning models to achieve high classification performance with imbalanced data sets. To conquer the problem, this study presents a strategy involving the reduction of size of majority data set and the generation of synthetic samples of minority data set. Parkinson's disease data set is used to examine and to compare the performance of classification methods. The paired t-tests are also used to show the effectiveness of the proposed method comparing with that of the other methods.",
author = "Yeh, {Chun Wu} and Li, {Der Chiang} and Lin, {Liang Sian} and Tsai, {Tung I.}",
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Yeh, CW, Li, DC, Lin, LS & Tsai, TI 2016, A learning approach with under-and over-sampling for imbalanced data sets. 於 A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (編輯), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557706, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., 頁 725-729, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16-07-10. https://doi.org/10.1109/IIAI-AAI.2016.20

A learning approach with under-and over-sampling for imbalanced data sets. / Yeh, Chun Wu; Li, Der Chiang; Lin, Liang Sian; Tsai, Tung I.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編輯 / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 725-729 7557706 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

研究成果: Conference contribution

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AB - It is difficult for learning models to achieve high classification performance with imbalanced data sets. To conquer the problem, this study presents a strategy involving the reduction of size of majority data set and the generation of synthetic samples of minority data set. Parkinson's disease data set is used to examine and to compare the performance of classification methods. The paired t-tests are also used to show the effectiveness of the proposed method comparing with that of the other methods.

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Yeh CW, Li DC, Lin LS, Tsai TI. A learning approach with under-and over-sampling for imbalanced data sets. 於 Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, 編輯, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 725-729. 7557706. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). https://doi.org/10.1109/IIAI-AAI.2016.20