International Journal of Computational Intelligence Systems

Volume 8, Issue 3, June 2015, Pages 490 - 501

Neural Incremental Attribute Learning in Groups

Authors
Fangzhou Liu, Ting Wang, Sheng-Uei Guan, Ka Lok Man
Corresponding Author
Fangzhou Liu
Available Online 1 June 2015.
DOI
https://doi.org/10.1080/18756891.2015.1023587How to use a DOI?
Keywords
Incremental Attribute Learning, Feature Ordering, Feature Grouping, Neural Networks, Pattern Classification, Feature Discrimination Ability
Abstract
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.

Download article (PDF)

Journal
International Journal of Computational Intelligence Systems
Volume-Issue
8 - 3
Pages
490 - 501
Publication Date
2015/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2015.1023587How to use a DOI?
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  - 2015
DA  - 2015/06
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  - Liu2015
ER  -