The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis

Part of the Springer Optimization and Its Applications book series (SOIA, volume 43)


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.


State Space Data Mining Linear Discriminant Analysis Boolean Function Breast Cancer Diagnosis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Vyborny, C., and M. Giger, (1994), “Computer Vision and Artificial Intelligence in Mammography,” AJR, Vol. 162, pp. 699–708.Google Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceLouisiana State UniversityBaton RougeUSA

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