Using genetic algorithms to optimise rough set partition sizes for HIV data analysis

Bodie Crossingham, Tshilidzi Marwala

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

18 Citations (Scopus)


In this paper, we present a method to optimise rough set partition sizes, to which rule extraction is performed on HIV (Human Immunodeficiency Virus) data. The genetic algorithm optimisation technique is used to determine the partition sizes of a rough set in order to maximise the rough sets prediction accuracy. The proposed method is tested on a set of six demographic properties of individuals obtained from the South African antenatal survey, with the outcome or decision being either HIV positive or negative. Rough set theory is chosen based on the fact that it is easy to interpret the extracted rules. The prediction accuracy of equal width bin partitioning is 69.8% while the accuracy achieved after optimising the partitions is 87.5%.

Original languageEnglish
Title of host publicationAdvances in Intelligent and Distributed Computing
Subtitle of host publicationProceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007
EditorsCostin Badica, Marcin Paprzycki
Number of pages6
Publication statusPublished - 2008
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


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