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

In: Data Mining and Knowledge Discovery via Logic-Based Methods

Author

Listed:
  • Evangelos Triantaphyllou

    (Louisiana State University)

Abstract

Almost any use of a data mining and sub data mining sub knowledge discovery, see data mining 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 sub accuracy rate accuracy rate into two rates: the sub false-positive false-positive and sub false-negative 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.

Suggested Citation

  • Evangelos Triantaphyllou, 2010. "The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis," Springer Optimization and Its Applications, in: Data Mining and Knowledge Discovery via Logic-Based Methods, chapter 0, pages 173-190, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-1630-3_9
    DOI: 10.1007/978-1-4419-1630-3_9
    as

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