Extending sample information for small data set prediction

Hung Yu Chen, Der Chiang Li, Liang Sian Lin

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

2 Citations (Scopus)

Abstract

This paper proposes a method that focuses on creating new data attributes by using fuzzy operations for solving small dataset learning problems. Using the idea of fuzzy rules, the membership value of antecedents in each rule can be extracted from the data point. Therefore, in this research, those membership values will be deemed as new data features and the data dimensionality will be extended. To test the effectiveness of the proposed method, the data set with new data features and the one with no special treatment will be utilized respectively to build predictive models. Paired t-test is carried out to see how effective the proposed method can improve the learning on the basis of small sample sets.

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.
Pages710-714
Number of pages5
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

Fuzzy rules

All Science Journal Classification (ASJC) codes

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

Cite this

Chen, H. Y., Li, D. C., & Lin, L. S. (2016). Extending sample information for small data set prediction. In A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (Eds.), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 710-714). [7557703] (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.2016.16
Chen, Hung Yu ; Li, Der Chiang ; Lin, Liang Sian. / Extending sample information for small data set prediction. 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. 710-714 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).
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abstract = "This paper proposes a method that focuses on creating new data attributes by using fuzzy operations for solving small dataset learning problems. Using the idea of fuzzy rules, the membership value of antecedents in each rule can be extracted from the data point. Therefore, in this research, those membership values will be deemed as new data features and the data dimensionality will be extended. To test the effectiveness of the proposed method, the data set with new data features and the one with no special treatment will be utilized respectively to build predictive models. Paired t-test is carried out to see how effective the proposed method can improve the learning on the basis of small sample sets.",
author = "Chen, {Hung Yu} and Li, {Der Chiang} and Lin, {Liang Sian}",
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Chen, HY, Li, DC & Lin, LS 2016, Extending sample information for small data set prediction. in A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (eds), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557703, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 710-714, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16-07-10. https://doi.org/10.1109/IIAI-AAI.2016.16

Extending sample information for small data set prediction. / Chen, Hung Yu; Li, Der Chiang; Lin, Liang Sian.

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. 710-714 7557703 (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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T1 - Extending sample information for small data set prediction

AU - Chen, Hung Yu

AU - Li, Der Chiang

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PY - 2016/8/31

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N2 - This paper proposes a method that focuses on creating new data attributes by using fuzzy operations for solving small dataset learning problems. Using the idea of fuzzy rules, the membership value of antecedents in each rule can be extracted from the data point. Therefore, in this research, those membership values will be deemed as new data features and the data dimensionality will be extended. To test the effectiveness of the proposed method, the data set with new data features and the one with no special treatment will be utilized respectively to build predictive models. Paired t-test is carried out to see how effective the proposed method can improve the learning on the basis of small sample sets.

AB - This paper proposes a method that focuses on creating new data attributes by using fuzzy operations for solving small dataset learning problems. Using the idea of fuzzy rules, the membership value of antecedents in each rule can be extracted from the data point. Therefore, in this research, those membership values will be deemed as new data features and the data dimensionality will be extended. To test the effectiveness of the proposed method, the data set with new data features and the one with no special treatment will be utilized respectively to build predictive models. Paired t-test is carried out to see how effective the proposed method can improve the learning on the basis of small sample sets.

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Chen HY, Li DC, Lin LS. Extending sample information for small data set prediction. 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. 710-714. 7557703. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). https://doi.org/10.1109/IIAI-AAI.2016.16