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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References
Booker, L.B., Goldberg, D.E., Holland, J.F.: Classifier systems and genetic algorithms. In: Carbonell, J.G. (ed.) Machine Learning. Paradigms and Methods, pp. 235–282. The MIT Press, Menlo Park (1990)
Grzymala-Busse, J.W.: Knowledge acquisition under uncertainty—A rough set approach. Journal of Intelligent & Robotic Systems 1, 3–16 (1988)
Grzymala-Busse, J.W.: LERS—A system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)
Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2002, Annecy, France, July 1–5, 2002, pp. 243–250 (2002)
Grzymala-Busse, J.W., et al.: An approach to imbalanced data sets based on changing rule strength. In: Learning from Imbalanced Data Sets, AAAI Workshop at the 17th Conference on AI, AAAI-2000, Austin, TX, July 30–31, 2000, pp. 69–74 (2000)
Grzymala-Busse, J.W., Hippe, Z.S., Mroczek, T.: Belief rules vs. decision rules: A preliminary appraisal of the problem. In: Intelligent Information Processing and Web Mining. Advances in Soft Computing, pp. 431–435. Springer, Heidelberg (2005)
Heckerman, D.: A tutorial on learning Bayesian networks. Microsoft Corporation, Technical Report MSR-TR-95-06 (1996)
Hippe, Z.S., Mroczek, T.: Melanoma classification and prediction using belief networks. In: Kurzynski, M., Puchala, E., Wozniak, M. (eds.) Computer Recognition Systems, pp. 337–342. Wroclaw University of Technology Press, Wroclaw (2003)
Holland, J.H., Holyoak, K.J., Nisbett, R.E.: Induction. Processes of Inference, Learning, and Discovery. The MIT Press, Menlo Park (1986)
Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001)
Kordek, A., et al.: Umbilical cord blood serum procalcitonin concentration in the diagnosis of early neonatal infection. J. Perinatol. 23, 148–153 (2003)
Meisner, M.: Procalcitonin. A New, Innovative Infection Parameter. Georg Thieme Verlag, Stuttgart (2000)
Mroczek, T., Grzymala-Busse, J.W., Hippe, Z.S.: Rules from belief networks: A Rough Set Approach. In: Tsumoto, S., et al. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 483–487. Springer, Heidelberg (2004)
Nyamande, K., Lalloo, U.G.: Serum procalcitonin distinguishes CAP due to bacteria, mycobacterium tuberculosis and PJP. Crit. Care Resc. 3, 236–243 (2001)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Podraza, W., et al.: Rough set methodology in supporting neonatal infection diagnosis. In: Cader, A., et al. (eds.) Artificial Intelligence and Soft Computing, pp. 281–287. Exit (2006)
Stoll, B.J., Gordon, T., Korones, S.B., et al.: Early-onset sepsis in very low birth weight neonates: A report from the National Institute of Child Health and Human Development Neonatal Research Network. J. Pediatr. 129, 72–80 (1996)
Tollner, U.: Early diagnosis of septicaemia in the newborn: clinical studies and sepsis score. Eur. J. Pediatr. 138, 331–337 (1982)
Varmuza, K., et al.: Comparison of consistent and inconsistent models in biomedical domain: A rough set approach to melanoma data. In: Methods of Artificial Intelligence, pp. 323–328. Silesian University of Technology Press, Gliwice (2003)
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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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