Abstract
This paper compares the accuracy of combined classifiers in medical data bases to the same knowledge discovery techniques applied to generic data bases. Specifically, we apply Bagging and Boosting methods for 16 medical and 16 generic data bases and compare the accuracy results with a more traditional approach (C4.5 algorithm). Bagging and Boosting methods are applied using different numbers of classifiers and the accuracy is computed using a cross-validation technique. This paper main contribution resides in recommend the most accurate method and possible parameterization for medical data bases and an initial identification of some characteristics that make medical data bases different from generic ones.
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Lopes, L., Scalabrin, E.E., Fernandes, P. (2008). An Empirical Study of Combined Classifiers for Knowledge Discovery on Medical Data Bases. In: Ishikawa, Y., et al. Advanced Web and Network Technologies, and Applications. APWeb 2008. Lecture Notes in Computer Science, vol 4977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89376-9_11
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DOI: https://doi.org/10.1007/978-3-540-89376-9_11
Publisher Name: Springer, Berlin, Heidelberg
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