Entropic feature discrimination ability for pattern classification based on neural IAL

Authors:
Ting Wang;Sheng-Uei Guan;Fei Liu
Affiliations:
Department of Computer Science, University of Liverpool, Liverpool, UK,Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China;Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China;Department of Computer Science & Computer Engineering, La Trobe University, Victoria, Australia
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

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Abstract

Incremental Attribute Learning (IAL) is a novel machine learning strategy, where features are gradually trained in one or more according to some orderings. In IAL, feature ordering is a special preprocessing. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on Discriminability, a distribution-based metric, and Entropy is presented to give ranks for feature ordering, which has been validated in both two-category and multivariable classification problems by neural networks. Final experimental results show that the new metric is not only applicable for IAL, but also able to obtain better performance in lower error rates.