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

Der-Chiang Li, I. Hsiang Wen, Chih Chieh Chang

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

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
Pages469-472
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 - Macau, China
Duration: 2013 Nov 152013 Nov 17

Other

Other2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
CountryChina
CityMacau
Period13-11-1513-11-17

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Hardware and Architecture
  • Control and Systems Engineering

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