Difference between revisions of "...find outliers"
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| + | Anomaly detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different. | ||
| + | _______________________________________________. | ||
| + | |||
Do you have > 100 features? | Do you have > 100 features? | ||
* Yes, then try [[One-class Support Vector Machine (SVM)]] | * Yes, then try [[One-class Support Vector Machine (SVM)]] | ||
* No, need fast training, then try [[Principal Components Analysis (PCA)-based Anomaly Detection]] | * No, need fast training, then try [[Principal Components Analysis (PCA)-based Anomaly Detection]] | ||
Revision as of 14:34, 2 June 2018
Anomaly detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different. _______________________________________________.
Do you have > 100 features?
- Yes, then try One-class Support Vector Machine (SVM)
- No, need fast training, then try Principal Components Analysis (PCA)-based Anomaly Detection
