Neural Incremental Attribute Learning in Groups

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.

Keywords

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