An improved active learning method based on feature selection
Chunjiang Fu, Liang Gong, Yupu Yang
Available Online May 2015.
- 10.2991/asei-15.2015.37How to use a DOI?
- active learning, support vector machine, principal component analysis, PCA
An improved active learning method taking advantage of feature selection technique is proposed. In early stages of active learning, the whole dataset is described using only the few key features, so that its overall distribution characteristic can be learned easily, reducing active learning’s possibility of falling into bad local optimum. As active learning proceeds, more and more data get labeled. Only then are detailed features of the dataset gradually added to further enhance the model’s classification performance. Experiments show that it is more efficient and more robust than traditional technique.
- © 2015, 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 - Chunjiang Fu AU - Liang Gong AU - Yupu Yang PY - 2015/05 DA - 2015/05 TI - An improved active learning method based on feature selection BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 170 EP - 174 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.37 DO - 10.2991/asei-15.2015.37 ID - Fu2015/05 ER -