Entropic feature discrimination ability for pattern classification based on neural IAL

Ting Wang*, Sheng Uei Guan, Fei Liu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings
Number of pages8
EditionPART 2
Publication statusPublished - 2012
Event9th International Symposium on Neural Networks, ISNN 2012 - Shenyang, China
Duration: 11 Jul 201214 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7368 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Symposium on Neural Networks, ISNN 2012


  • discrimination ability
  • entropy
  • feature ordering
  • incremental attribute learning
  • neural networks

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