Improving virtual sample generation for small sample learning with dependent attributes

Liang Sian Lin, Der Chiang Li, Chih Wei Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Since the product life cycles are getting shorter and shorter, the issue of small data set learning has drawn more and more attentions in both academics and enterprises. Many methods have been proposed to improve the learning performance of small data set. In these methods, the virtual sample generation approach is the most popular technique for improving small data learning. In the process of virtual sample generation, the attribute independence in small data is the key part to determine the learning performance, because it is the necessary assumption before generating virtual samples. However, in the real world, attributes in the data set usually are not mutual independent. Therefore, this paper proposes a new process to generate independent virtual samples based on the box-and-whisker plot domain estimation. In order to validate the effectiveness of the proposed method, one data set is used to calculate the classification accuracy average and standard deviation based on the support vector machine. The results of the experiment show that the presented method has a superior classification performance than other methods.

Original languageEnglish
Title of host publicationProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
EditorsAyako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages715-718
Number of pages4
ISBN (Electronic)9781467389853
DOIs
Publication statusPublished - 2016 Aug 31
Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
Duration: 2016 Jul 102016 Jul 14

Publication series

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

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
CountryJapan
CityKumamoto
Period16-07-1016-07-14

Fingerprint

Support vector machines
Life cycle
Industry
Experiments

All Science Journal Classification (ASJC) codes

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

Cite this

Lin, L. S., Li, D. C., & Pan, C. W. (2016). Improving virtual sample generation for small sample learning with dependent attributes. In A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (Eds.), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 715-718). [7557704] (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.2016.18
Lin, Liang Sian ; Li, Der Chiang ; Pan, Chih Wei. / Improving virtual sample generation for small sample learning with dependent attributes. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. editor / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 715-718 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).
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abstract = "Since the product life cycles are getting shorter and shorter, the issue of small data set learning has drawn more and more attentions in both academics and enterprises. Many methods have been proposed to improve the learning performance of small data set. In these methods, the virtual sample generation approach is the most popular technique for improving small data learning. In the process of virtual sample generation, the attribute independence in small data is the key part to determine the learning performance, because it is the necessary assumption before generating virtual samples. However, in the real world, attributes in the data set usually are not mutual independent. Therefore, this paper proposes a new process to generate independent virtual samples based on the box-and-whisker plot domain estimation. In order to validate the effectiveness of the proposed method, one data set is used to calculate the classification accuracy average and standard deviation based on the support vector machine. The results of the experiment show that the presented method has a superior classification performance than other methods.",
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Lin, LS, Li, DC & Pan, CW 2016, Improving virtual sample generation for small sample learning with dependent attributes. in A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (eds), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557704, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 715-718, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16-07-10. https://doi.org/10.1109/IIAI-AAI.2016.18

Improving virtual sample generation for small sample learning with dependent attributes. / Lin, Liang Sian; Li, Der Chiang; Pan, Chih Wei.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. ed. / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 715-718 7557704 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lin LS, Li DC, Pan CW. Improving virtual sample generation for small sample learning with dependent attributes. In Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, editors, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 715-718. 7557704. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). https://doi.org/10.1109/IIAI-AAI.2016.18