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arXiv · 1708.01035

Detection of Abnormal Input-Output Associations

Charmgil Hong·Siqi Liu·Milos Hauskrecht
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Abstract

We study a novel outlier detection problem that aims to identify abnormal input-output associations in data, whose instances consist of multi-dimensional input (context) and output (responses) pairs. We present our approach that works by analyzing data in the conditional (input--output) relation space, captured by a decomposable probabilistic model. Experimental results demonstrate the ability of our approach in identifying multivariate conditional outliers.

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Charmgil Hong, Siqi Liu, Milos Hauskrecht. 2017-08-03. Detection of Abnormal Input-Output Associations. https://arxiv.org/abs/1708.01035

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