Summary
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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References
A. Lasry, G. S. Zaric, and M. W. Carter. “Multi-level resource allocation for HIV prevention: A model for developing countries.” European Journal of Operational Research, vol. 180, p. 786–799, 2007.
“UNAIDS.” http://www.unaids.org/en/HIV_data/2006GlobalReport/default.asp/. Last accessed: 20/3/2007.
K. E. Poundstone, S. A. Strathdee, and D. D. Celentano. “The Social Epidemiology of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome.” Epidemiol Reviews, vol. 26, pp. 22–35, 2004.
T. Rowland, Ohno-Machado, and A. Ohrn. “Comparison of multiple prediction models for ambulation following spinal cord injury.” In Chute, vol. 31, pp. 528–532, 1998.
Z. Pawlak. Rough Sets, Theoretical Aspects of Reasoning about Data, chap. 3, p. 33. Kluwer Academic Publishers, 1991.
M. Inuiguchi and T. Miyajima. “Rough set based rule induction from two decision tables.” European Journal of Operational Research, vol. In Press, Corrected Proof, 2006.
R. Department of Health. “National HIV and Syphilis Sero-Prevalence Survey of Women Attending Public Antenatal Clinics in South Africa.” http://www.info.gov.za/otherdocs/2002/hivsurvey01.pdf, 2001.
A. Ohrn and T. Rowland. “Rough Sets: A Knowledge Discovery Technique for Multifactorial Medical Outcomes.” American Journal of Physical Medicine and Rehabilitation, vol. 79, pp. 100–108, 2000.
F. Witlox and H. Tindemans. “The application of rough sets analysis in activity-based modelling. Opportunities and constraints.” Expert Systems with Applications, vol. 27, p. 585–592, 2004.
S. Malve and R. Uzsoy. “A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families.” Computers and Operations Research, vol. 34, p. 3016–3028, 2007.
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Crossingham, B., Marwala, T. (2008). Using Genetic Algorithms to Optimise Rough Set Partition Sizes for HIV Data Analysis. In: Badica, C., Paprzycki, M. (eds) Advances in Intelligent and Distributed Computing. Studies in Computational Intelligence, vol 78. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74930-1_25
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DOI: https://doi.org/10.1007/978-3-540-74930-1_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74929-5
Online ISBN: 978-3-540-74930-1
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