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Novelty detection is one of the classic problems in machine learning that has applications across several domains. This paper proposes a novel autoencoder based on Deep Gaussian Processes for novelty detection tasks. Learning the proposed model is made tractable and scalable through the use of random feature approximations and stochastic variational inference. The result is a flexible model that is...
We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to...
Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. If rule-based systems and supervised methods usually provide the best detection and prevention, labelled training datasets are often non-existent and such solutions lack reactivity when facing adaptive fraudsters. Many generic...
This paper demonstrates how we applied a constraint-based dynamic adaptation approach on CarDemo, a traffic management system. The approach allows domain experts to describe the adaptation goals as declarative constraints, and automatically plan the adaptation decisions to satisfy these constraints. We demonstrate how to utilise this approach to realise the dynamic switch of routing services of the...
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