Abstract
This paper presents results of experiments on data sets that were subjected to increasing incompleteness by random replacement of attribute values by symbols of missing attribute values. During these experiments the total error rate and error rates for all concepts, results of repeated 30 times ten-fold cross validation, were recorded. We observed that for some data sets increased incompleteness might result in a significant improvement for the total error rate and sensitivity (with the significance level of 5%, two-tailed test). These results may be applied for improving data mining techniques, especially for domains in which sensitivity is important, e.g., in medical area.
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Grzymala-Busse, J.W., Marepally, S.R. (2010). Sensitivity and Specificity for Mining Data with Increased Incompleteness. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_45
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DOI: https://doi.org/10.1007/978-3-642-13208-7_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13207-0
Online ISBN: 978-3-642-13208-7
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