Abstract
Almost any use of a data mining and knowledge discovery method on a data set requires some discussion on the accuracy of the extracted model on some test data. This accuracy can be a general description of how well the extracted model classifies test data. Some studies split this accuracy rate into two rates: the false-positive and false-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is one thing to wrongly diagnose a benign tumor as malignant than the other way around. Related are some of the discussions in Sections 1.3.4, 4.5, and 11.6.
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Triantaphyllou, E. (2010). The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis. In: Data Mining and Knowledge Discovery via Logic-Based Methods. Springer Optimization and Its Applications, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1630-3_9
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DOI: https://doi.org/10.1007/978-1-4419-1630-3_9
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