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 language | English |
|---|---|
| Title of host publication | Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 |
| Pages | 469-472 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 - Macau, China Duration: 2013 Nov 15 → 2013 Nov 17 |
Other
| Other | 2013 24th IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013 |
|---|---|
| Country | China |
| City | Macau |
| Period | 13-11-15 → 13-11-17 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Computer Science Applications
- Hardware and Architecture
- Control and Systems Engineering
Cite this
}
A new virtual-sample-generating method based on the heuristics algorithm. / Li, Der-Chiang; Wen, I. Hsiang; Chang, Chih Chieh.
Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013. 2013. p. 469-472 6714829.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A new virtual-sample-generating method based on the heuristics algorithm
AU - Li, Der-Chiang
AU - Wen, I. Hsiang
AU - Chang, Chih Chieh
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84897885509&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897885509&partnerID=8YFLogxK
U2 - 10.1109/GSIS.2013.6714829
DO - 10.1109/GSIS.2013.6714829
M3 - Conference contribution
AN - SCOPUS:84897885509
SN - 9781467352628
SP - 469
EP - 472
BT - Proceedings of 2013 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2013
ER -