Detection of Abnormal Input-Output Associations
Artificial Intelligence
2017-08-04 v1
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.
Keywords
Cite
@article{arxiv.1708.01035,
title = {Detection of Abnormal Input-Output Associations},
author = {Charmgil Hong and Siqi Liu and Milos Hauskrecht},
journal= {arXiv preprint arXiv:1708.01035},
year = {2017}
}