NDLI: A new virtual-sample-generating method based on the heuristics algorithm

Content Provider	IEEE Xplore Digital Library
Author	Der-Chiang Li I-Hsiang Wen Chih-Chieh Chang
Copyright Year	2013
Description	Author affiliation: Dept. of Ind. & Inf. Manage., Nat. Cheng Kung Univ., Tainan, Taiwan (Der-Chiang Li; I-Hsiang Wen; Chih-Chieh Chang)
Abstract	While back-propagation neural networks (BPNN) are effective learning tools for building non-linear models, they are often unstable when using small-data-sets. Therefore, in order to solve this problem, we construct artificial samples, called virtual samples, to improve the learning robustness. This research develops a novel method of virtual sample generation (VSG), named genetic algorithm-based virtual sample generation (GABVSG), which considers the integrated effects and constraints of data attributes. We first determine the acceptable range by using MTD functions, and construct the feasibility-based programming (FBP) model with BPNN. A genetic algorithm (GA) is then applied to accelerate the generation of feasible virtual samples. Finally, we use two real cases to verify the performance of the proposed method by comparing the results with those of two forecasting models, BPNN and support vector machine for regression (SVR). The experimental results indicate that the performance of the GABVSG method is superior to that of using original training data without artificial samples. Consequently, the proposed method can improve learning performance significantly when working with small samples.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	469
Ending Page	472
File Size	166393
Page Count	4
File Format	PDF
ISSN	21669430
e-ISBN	9781467352482
DOI	10.1109/GSIS.2013.6714829
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Powders Small data set Predictive models Bladder Ceramics genetic algorithm-based virtual sample generation (GABVSG) Forecasting feasibility-based programming (FBP) model Cancer
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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