• Jun 14, 2017 News!Vol.6, No.3 has been indexed by EI(Inspec)!   [Click]
  • May 03, 2016 News!Vol.5, No.5 has been indexed by EI(Inspec)!   [Click]
  • May 03, 2016 News!Vol.5, No.4 has been indexed by EI(Inspec)!   [Click]
General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(6): 807-811 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.242

Correlation-based Feature Ordering for Classification based on Neural Incremental Attribute Learning

Ting Wang, Sheng-Uei Guan, and Fei Liu
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: steven.guan@xjtlu.edu.cn).
Fei Liu is with the Department of Computer Science and Computer Engineering, La Trobe University, Victoria, 3086, Australia (e-mail: f.liu@latrobe.edu.au).

[PDF]

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.

Copyright © 2008-2015. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net