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Outlier Detection I: Supervised Methods

  • Modeling outlier detection as a classification problem
    • Samples examined by domain experts used for training & testing
  • Methods for Learning a classifier for outlier detection effectively:
    • Model normal objects & report those not matching the model as outliers, or
    • Model outliers and treat those not matching the model as normal
  • Challenges
    • Imbalanced classes, i.e., outliers are rare:
      • Boost the outlier class and make up some artificial outliers
    • Catch as many outliers as possible
      • recall is more important than accuracy (i.e., not mislabeling normal objects as outliers)

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