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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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