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Multi-criteria Anomaly Detection using Pareto Depth Analysis

Multi-criteria Anomaly Detection using Pareto Depth Analysis

This video was recorded at Video Journal of Machine Learning Abstracts - Volume 3. We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria by taking some linear combination of them. If the importance of the different criteria are not known in advance, the algorithm may need to be executed multiple times with different choices of weights in... Show More
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