Abstract—Incremental Attribute Learning (IAL) is a novel
supervised machine learning approach, which sequentially
trains features one by one. Thus feature ordering is very
important to IAL. Previous studies on feature ordering only
concentrated on the contribution of each feature to different
outputs. However, besides contribution, correlations among
input features and output categories are also very important to
the final classification result, which has not yet been researched
in feature ordering but has confirmed in multivariate statistics.
This study aims to find out the relations between feature
ordering and feature correlations. This paper presents a new
method for feature ordering calculation which is based on
correlations between input features and outputs. Experimental
results confirm that correlation-based feature ordering can
produce better classification results than contribution-based
approaches, feature orderings with theoriginal sequence sorted
in the database, and conventional methods where all features
are trained in one batch.
Index Terms—Machine learning, incremental attribute learning, pattern classification, feature ordering, correlation.
Ting Wang is with the Department of Computer Science, University of Liverpool, Liverpool,L69 3BX, UK, and the Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China (e-mail: ting.wang@ liverpool.ac.uk).
Sheng-Uei Guanis with the Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China. (e-mail: firstname.lastname@example.org).
Fei Liu is with the Department of Computer Science and Computer Engineering, La Trobe University, Victoria, 3086, Australia (e-mail: email@example.com).
Cite:Ting Wang, Sheng-Uei Guan, and Fei Liu, "Correlation-based Feature Ordering for Classification based on Neural Incremental Attribute Learning," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 807-811, 2012.