Non-parametric statistical assistance in virtual sample selection for small data set prediction

Yao San Lin; Liang Sian Lin; Der Chiang Li; Wei Lin Liao

doi:10.1109/ACIT-CSI.2015.70

Non-parametric statistical assistance in virtual sample selection for small data set prediction

Yao San Lin, Liang Sian Lin, Der Chiang Li, Wei Lin Liao

Institute of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of the generated virtual samples, such that the prediction model can be more robust with the basis of high quality virtual samples. Experimental results show that the prediction model with statistical test procedure performs better than the original one, with more stable and improved accuracies, and the examination procedure can effectively lower the prediction error.

Original language	English
Title of host publication	Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
Editors	Kensei Tsuchida, Naohiro Ishii, Takaaki Goto, Satoshi Takahashi
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	369-373
Number of pages	5
ISBN (Electronic)	9781467396424
DOIs	https://doi.org/10.1109/ACIT-CSI.2015.70
Publication status	Published - 2015 Nov 23
Event	3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015 - Okayama, Japan Duration: 2015 Jul 12 → 2015 Jul 16

Publication series

Name	Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015

Other

Other	3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
Country/Territory	Japan
City	Okayama
Period	15-07-12 → 15-07-16

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Human-Computer Interaction
Information Systems

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10.1109/ACIT-CSI.2015.70

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Lin, Y. S., Lin, L. S., Li, D. C., & Liao, W. L. (2015). Non-parametric statistical assistance in virtual sample selection for small data set prediction. In K. Tsuchida, N. Ishii, T. Goto, & S. Takahashi (Eds.), Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015 (pp. 369-373). Article 7336090 (Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACIT-CSI.2015.70

Lin, Yao San ; Lin, Liang Sian ; Li, Der Chiang et al. / Non-parametric statistical assistance in virtual sample selection for small data set prediction. Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015. editor / Kensei Tsuchida ; Naohiro Ishii ; Takaaki Goto ; Satoshi Takahashi. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 369-373 (Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015).

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title = "Non-parametric statistical assistance in virtual sample selection for small data set prediction",

abstract = "Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of the generated virtual samples, such that the prediction model can be more robust with the basis of high quality virtual samples. Experimental results show that the prediction model with statistical test procedure performs better than the original one, with more stable and improved accuracies, and the examination procedure can effectively lower the prediction error.",

author = "Lin, {Yao San} and Lin, {Liang Sian} and Li, {Der Chiang} and Liao, {Wei Lin}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015 ; Conference date: 12-07-2015 Through 16-07-2015",

year = "2015",

month = nov,

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doi = "10.1109/ACIT-CSI.2015.70",

language = "English",

series = "Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "369--373",

editor = "Kensei Tsuchida and Naohiro Ishii and Takaaki Goto and Satoshi Takahashi",

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Lin, YS, Lin, LS, Li, DC & Liao, WL 2015, Non-parametric statistical assistance in virtual sample selection for small data set prediction. in K Tsuchida, N Ishii, T Goto & S Takahashi (eds), Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015., 7336090, Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015, Institute of Electrical and Electronics Engineers Inc., pp. 369-373, 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015, Okayama, Japan, 15-07-12. https://doi.org/10.1109/ACIT-CSI.2015.70

Non-parametric statistical assistance in virtual sample selection for small data set prediction. / Lin, Yao San; Lin, Liang Sian; Li, Der Chiang et al.
Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015. ed. / Kensei Tsuchida; Naohiro Ishii; Takaaki Goto; Satoshi Takahashi. Institute of Electrical and Electronics Engineers Inc., 2015. p. 369-373 7336090 (Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Non-parametric statistical assistance in virtual sample selection for small data set prediction

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AU - Lin, Liang Sian

AU - Li, Der Chiang

AU - Liao, Wei Lin

PY - 2015/11/23

Y1 - 2015/11/23

N2 - Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of the generated virtual samples, such that the prediction model can be more robust with the basis of high quality virtual samples. Experimental results show that the prediction model with statistical test procedure performs better than the original one, with more stable and improved accuracies, and the examination procedure can effectively lower the prediction error.

AB - Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of the generated virtual samples, such that the prediction model can be more robust with the basis of high quality virtual samples. Experimental results show that the prediction model with statistical test procedure performs better than the original one, with more stable and improved accuracies, and the examination procedure can effectively lower the prediction error.

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Lin YS, Lin LS, Li DC, Liao WL. Non-parametric statistical assistance in virtual sample selection for small data set prediction. In Tsuchida K, Ishii N, Goto T, Takahashi S, editors, Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 369-373. 7336090. (Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015). doi: 10.1109/ACIT-CSI.2015.70

Non-parametric statistical assistance in virtual sample selection for small data set prediction

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