Abstract
This paper presents the results of our experiments on a data set describing neonatal infection. We used two main tools: the MLEM2 algorithm of rule induction and BeliefSEEKER system for generation of Bayesian nets and rule sets. Both systems are based on rough set theory. Our main objective was to compare the quality of diagnosis of cases from two testing data sets: with an additional attribute called PCT and without this attribute. The PCT attribute was computed using constructive induction. The best results were associated with the rule set induced by the MLEM2 algorithm and testing data set enhanced by constructive induction.
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Grzymala-Busse, J.W., Hippe, Z.S., Kordek, A., Mroczek, T., Podraza, W. (2007). Neonatal Infection Diagnosis Using Constructive Induction in Data Mining. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_34
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DOI: https://doi.org/10.1007/978-3-540-72530-5_34
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