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Stochastic Outlier Selection

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Anomaly detection is relevant for many tasks, ranging from detecting credit card fraud to terrorist threats. Typically, outlier-selection algorithms are used for detecting anomalies. First, we briefly explain the difference between anomalies and outliers. Subsequently, we present a novel, unsupervised outlier-selection algorithm, called Stochastic Outlier Selection (SOS). The SOS algorithm computes for each data point an outlier probability. These probabilities are much more intuitive than the unbounded outlier scores computed by existing outlier-selection algorithms. We evaluate SOS on a variety of real-world and synthetic datasets, and compare it to four state-of-the-art outlier-selection algorithms. Our results show that SOS has a superior performance while being more robust to data perturbations and parameter settings. We conclude that SOS is an effective algorithm to select outliers in a dataset that compares favorably to state-of-the-art outlier-selection algorithms.

This is work with Eric O. Postma.

http://www.jeroenjanssens.com

This talk is part of the Machine Learning @ CUED series.

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