Ant colony optimization of rough set for HV bushings fault detection

L. J. Mpanza, T. Marwala

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

Abstract

In this paper we propose the optimization of Rough Set method using ant colony for oil-impregnated paper bushings. Ant colony is used to discretize the training data set. The ant colony optimized rough set is compare to a rough set who's data is discretized using equal frequency bin (EFB). Ant colony optimized (ACO) rough set results show an improvement compared to the EFB. The ACO rough set has an accuracy 4% high than that of EFB rough set. Rules generated are only a third for ACO compared to EFB. Although ACO takes longer to train, it proves to outperform EFB in all other respects.

Original languageEnglish
Title of host publicationProceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI 2011
Pages97-102
Number of pages6
DOIs
Publication statusPublished - 2011
Event4th International Workshop on Advanced Computational Intelligence, IWACI 2011 - Wuhan, Hubei, China
Duration: 19 Oct 201121 Oct 2011

Publication series

NameProceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI 2011

Conference

Conference4th International Workshop on Advanced Computational Intelligence, IWACI 2011
Country/TerritoryChina
CityWuhan, Hubei
Period19/10/1121/10/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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