Neural Incremental Attribute Learning in Groups

Fangzhou Liu, Ting Wang*, Sheng Uei Guan, Ka Lok Man

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)490-501
Number of pages12
JournalInternational Journal of Computational Intelligence Systems
Issue number3
Publication statusPublished - 4 May 2015
Externally publishedYes


  • Feature Discrimination Ability
  • Feature Grouping
  • Feature Ordering
  • Incremental Attribute Learning
  • Neural Networks
  • Pattern Classification

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