Volume 8, Issue 3, June 2015, Pages 490 - 501
Neural Incremental Attribute Learning in Groups
- Fangzhou Liu, Ting Wang, Sheng-Uei Guan, Ka Lok Man
- Corresponding Author
- Fangzhou Liu
Available Online 9 January 2017.
- https://doi.org/10.1080/18756891.2015.1023587How to use a DOI?
- Incremental Attribute Learning, Feature Ordering, Feature Grouping, Neural Networks, Pattern Classification, Feature Discrimination Ability
- Incremental Attribute Learning (IAL) is a feasible approach for solving high-dimensional pattern recognition problems. It gradually trains features one by one. Previous research indicated that supervised machine learning with input attribute ordering can improve classification results. Moreover, input space partitioning can also effectively reduce the interference among features. This study proposed IAL based on Grouped Feature Ordering, which fused feature partitioning with feature ordering. The experimental results show that this approach is not only applicable for pattern classification improvement, but also efficient to reduce interference among features.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Fangzhou Liu AU - Ting Wang AU - Sheng-Uei Guan AU - Ka Lok Man PY - 2017 DA - 2017/01 TI - Neural Incremental Attribute Learning in Groups JO - International Journal of Computational Intelligence Systems SP - 490 EP - 501 VL - 8 IS - 3 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1023587 DO - https://doi.org/10.1080/18756891.2015.1023587 ID - Liu2017 ER -