Detection of Abnormal Input-Output Associations
This addresses outlier detection for data with multi-dimensional input-output pairs, but appears incremental as it builds on existing probabilistic modeling approaches.
The paper tackles the problem of detecting abnormal input-output associations in data by analyzing conditional relations using a decomposable probabilistic model, demonstrating its ability to identify multivariate conditional outliers in experiments.
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.