Please use this identifier to cite or link to this item:
Title: A generative adversarial network structure for learning with small numerical data sets
Authors: Li, Der-Chiang
Chen, Szu-Chou
Lin, Yao-Sin
Huang, Kuan-Cheng
Keywords: Humanities::Language
Issue Date: 2021
Source: Li, D., Chen, S., Lin, Y. & Huang, K. (2021). A generative adversarial network structure for learning with small numerical data sets. Applied Sciences, 11(22), 10823-.
Journal: Applied Sciences 
Abstract: In recent years, generative adversarial networks (GANs) have been proposed to generate simulated images, and some works of literature have applied GAN to the analysis of numerical data in many fields, such as the prediction of building energy consumption and the prediction and identification of liver cancer stages. However, these studies are based on sufficient data volume. In the current era of globalization, the demand for rapid decision‐making is increasing, but the data available in a short period of time is scarce. As a result, machine learning may not provide precise results. Obtaining more information from a small number of samples has become an important issue. Therefore, this study aimed to modify the generative adversarial network structure for learning with small numerical datasets, starting with the Wasserstein GAN (WGAN) as the GAN architecture, and using mega‐trend‐diffusion (MTD) to limit the bound of virtual samples that the GAN generates. The model verification of our proposed structure was conducted with two datasets in the UC Irvine Machine Learning Repository, and the performance was evaluated using three criteria: accuracy, standard deviation, and p‐value. The experiment result shows that, using this improved GAN architecture (WGAN_MTD), small sample data can also be used to generate virtual samples that are similar to real samples through GAN.
ISSN: 2076-3417
DOI: 10.3390/app112210823
Rights: © 2021 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CCLC Journal Articles

Files in This Item:
File Description SizeFormat 
applsci-11-10823-v2.pdf1.27 MBAdobe PDFThumbnail

Page view(s)

Updated on May 17, 2022


Updated on May 17, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.