Summary
. This chapter describes experiments with a challenging data set describing preterm births. The data set, collected at the Duke University Medical Center, was large but at the same time many attribute values were missing. However, the main problem was that only 20.7% of the total number of cases represented the important preterm birth class. Thus, the data set was imbalanced. For comparison, we include results of experiments on another imbalanced data set, the well-known breast cancer data set.
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Grzymala-Busse, J.W., Goodwin, L.K., Grzymala-Busse, W.J., Zheng, X. (2004). An Approach to Imbalanced Data Sets Based on Changing Rule Strength. In: Pal, S.K., Polkowski, L., Skowron, A. (eds) Rough-Neural Computing. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18859-6_21
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DOI: https://doi.org/10.1007/978-3-642-18859-6_21
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