One-Class Classification Decomposition for Imbalanced Classification of Breast Cancer Malignancy Data

  • Bartosz Krawczyk
  • Łukasz Jeleń
  • Adam Krzyżak
  • Thomas Fevens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)


In this paper we address a problem arising from the classification of breast cancer malignancy data. Due to the fact that there is much smaller number of patients which are diagnosed with high malignancy, data sets are prone to have a high imbalance between malignancy classes. To overcome this problem we have applied state-of-the-art methods for imbalanced classification to our data set and demonstrate an improvement in the classification sensitivity. The achieved sensitivity for our data set was recorded at 92.34%.


one-class classification classifier ensemble pattern recognition image processing imbalanced classification breast cancer nuclei segmentation 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bartosz Krawczyk
    • 1
  • Łukasz Jeleń
    • 2
  • Adam Krzyżak
    • 3
  • Thomas Fevens
    • 3
  1. 1.Department of Systems and Computer NetworksWrocław University of TechnologyWrocławPoland
  2. 2.Institute of Computer Engineering, Control and RoboticsWrocław University of TechnologyWrocławPoland
  3. 3.Department of Computer Science and Software EngineeringConcordia UniversityMontréalCanada

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