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

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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
頁面469-472
頁數4
DOIs
出版狀態Published - 2013
事件2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 - Macau, China
持續時間: 2013 十一月 152013 十一月 17

Other

Other2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
國家/地區China
城市Macau
期間13-11-1513-11-17

All Science Journal Classification (ASJC) codes

  • 計算機理論與數學
  • 電腦科學應用
  • 硬體和架構
  • 控制與系統工程

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