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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

Speaker notes:

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