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

Bodie Crossingham; Tshilidzi Marwala

doi:10.1007/978-3-540-74930-1_25

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

Bodie Crossingham, Tshilidzi Marwala

University of the Witwatersrand

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Advances in Intelligent and Distributed Computing
Subtitle of host publication	Proceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007
Editors	Costin Badica, Marcin Paprzycki
Pages	245-250
Number of pages	6
DOIs	https://doi.org/10.1007/978-3-540-74930-1_25
Publication status	Published - 2008
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	78
ISSN (Print)	1860-949X

ASJC Scopus subject areas

Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-540-74930-1_25

Cite this

Crossingham, B., & Marwala, T. (2008). Using genetic algorithms to optimise rough set partition sizes for HIV data analysis. In C. Badica, & M. Paprzycki (Eds.), Advances in Intelligent and Distributed Computing: Proceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007 (pp. 245-250). (Studies in Computational Intelligence; Vol. 78). https://doi.org/10.1007/978-3-540-74930-1_25

Crossingham, Bodie ; Marwala, Tshilidzi. / Using genetic algorithms to optimise rough set partition sizes for HIV data analysis. Advances in Intelligent and Distributed Computing: Proceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007. editor / Costin Badica ; Marcin Paprzycki. 2008. pp. 245-250 (Studies in Computational Intelligence).

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Crossingham, B & Marwala, T 2008, Using genetic algorithms to optimise rough set partition sizes for HIV data analysis. in C Badica & M Paprzycki (eds), Advances in Intelligent and Distributed Computing: Proceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007. Studies in Computational Intelligence, vol. 78, pp. 245-250. https://doi.org/10.1007/978-3-540-74930-1_25

Using genetic algorithms to optimise rough set partition sizes for HIV data analysis. / Crossingham, Bodie; Marwala, Tshilidzi.
Advances in Intelligent and Distributed Computing: Proceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007. ed. / Costin Badica; Marcin Paprzycki. 2008. p. 245-250 (Studies in Computational Intelligence; Vol. 78).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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%.

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Crossingham B, Marwala T. Using genetic algorithms to optimise rough set partition sizes for HIV data analysis. In Badica C, Paprzycki M, editors, Advances in Intelligent and Distributed Computing: Proceedings of the 1st International Symposium on Intelligent and Distributed Computing IDC'2007, Craiova, Romania, October 2007. 2008. p. 245-250. (Studies in Computational Intelligence). doi: 10.1007/978-3-540-74930-1_25

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

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ASJC Scopus subject areas

UN SDGs

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