Abstract
The risk of hepatitis-C virus is considered as a challenge in the field of medicine. Applying feature reduction technique and generating rules based on the selected features were considered as an important step in data mining. It is needed by medical experts to analyze the generated rules to find out if these rules are important in real life cases. This paper presents an application of a rough set analysis to discover the dependency between the attributes, and to generate a set of reducts consisting of a minimal number of attributes. The experimental results obtained, show that the overall accuracy offered by the rough sets is high.
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© 2012 Springer-Verlag Berlin Heidelberg
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Zaki, A., Salama, M.A., Hefny, H., Hassanien, A.E. (2012). Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_6
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DOI: https://doi.org/10.1007/978-3-642-35326-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35325-3
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