Neural Incremental Attribute Learning in Groups

Autor:	Fangzhou Liu, Ting Wang, Sheng-Uei Guan, Ka Lok Man
Jazyk:	angličtina
Rok vydání:	2015
Předmět:	Incremental Attribute Learning Feature Ordering Feature Grouping Neural Networks Pattern Classification Feature Discrimination Ability Electronic computers. Computer science QA75.5-76.95
Zdroj:	International Journal of Computational Intelligence Systems, Vol 8, Iss 3 (2015)
Druh dokumentu:	article
ISSN:	18756891 1875-6883
DOI:	10.1080/18756891.2015.1023587
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/60df427411da4b35b463f47099997a5a Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ