Research on Sample Dataset Balance Method of SVM Based on GA
Available Online October 2016.
- 10.2991/mmme-16.2016.197How to use a DOI?
- SVM, Fault Diagnosis, Sample Balance, GA
SVM was widely used in fault diagnosis, and achieved good results. However, the unbalance between normal sample datasets and fault sample datasets made it very difficult to establish a proper diagnosis model. For ac-tual diagnosis, the normal samples are usually more than the fault ones, and it will lead to misdiagnosis. In this paper, a method based on GA to solve the imbalance problem for SVM is presented. In this method, the sam-ples are expanded by GA so that the number of normal sample datasets and fault sample datasets keeps bal-ance. The method of selecting parent samples is also studied. The experiments show that the method proposed in this paper improves the accuracy of diagnosis.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiao Han PY - 2016/10 DA - 2016/10 TI - Research on Sample Dataset Balance Method of SVM Based on GA BT - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/mmme-16.2016.197 DO - 10.2991/mmme-16.2016.197 ID - Han2016/10 ER -