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Classification-Based Method I: One-Class Model

  • Idea: Train a classification model that can distinguish “normal” data from outliers
  • A brute-force approach: Consider a training set that contains samples labeled as “normal” and others labeled as “outlier”
    • But, the training set is typically heavily biased: # of “normal” samples likely far exceeds # of outlier samples
    • Cannot detect unseen anomaly
  • One-class model: A classifier is built to describe only the normal class.
    • Learn the decision boundary of the normal class using classification methods such as SVM
    • Any samples that do not belong to the normal class (not within the decision boundary) are declared as outliers
    • Adv: can detect new outliers that may not appear close to any outlier objects in the training set
    • Extension: Normal objects may belong to multiple classes


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