Speaker
Description
In this work, we address the problem of binary classification under label uncertainty in settings where both feature-based and relational data are available. Motivated by applications in financial fraud detection, we propose a Bayesian Gaussian Process classification model that leverages covariate similarities and multilayer network structure. Our approach accounts for uncertainty in the observed labels during training, enabling robust inference and reliable out-of-sample prediction. We define a composite covariance function that integrates kernel representations over both covariates and network layers, effectively capturing different modes of similarity. To perform posterior inference, we use a first gradient marginal Metropolis-Hastings sampler, which improves sampling efficiency and reduces the need for tuning. The proposed methodology is validated on simulated data and applied to a real-world financial fraud detection task, demonstrating strong practical applicability with real financial data
| Classification | Both methodology and application |
|---|---|
| Keywords | Bayesian Classification, Gaussian Processes, Label Uncertainty, Multilayer Networks, Fraud Detection |