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Classification-Based Method II: Semi-Supervised Learning

  • Semi-supervised learning: Combining classification-based and clustering-based methods
  • Method
    • Using a clustering-based approach, find a large cluster, C, and a small cluster, C1
    • Since some objects in C carry the label “normal”, treat all objects in C as normal
    • Use the one-class model of this cluster to identify normal objects in outlier detection
    • Since some objects in cluster C1 carry the label “outlier”, declare all objects in C1 as outliers
    • Any object that does not fall into the model for C (such as a) is considered an outlier as well

  • Comments on classification-based outlier detection methods
    • Strength: Outlier detection is fast
    • Bottleneck: Quality heavily depends on the availability and quality of the training set, but often difficult to obtain representative and high-quality training data

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