1 research outputs found
Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-Learning Framework for Detecting Deviances in Business Process Instances
The problem of discovering an effective Deviance DetectionModel (DDM) out of log data, has been attracting
increasing attention in recent years in the very active research areas of Business Process Intelligence (BPI) and
of Process Mining. Such a model can be used to assess whether novel instances of the business process are
deviant or not, which is a hot topic in many application scenarios such as cybersecurity and fraud detection.
This paper extends a previous proposal where an innovative ensemble-learning framework for mining business
process deviances was introduced, hinging on multi-view learning scheme. Specifically, we introduce here an
alternative meta-learning method for probabilistically combining the predictions of different base DDMs. The
entire learning method is embedded into a conceptual system architecture that is meant to support the detection
and analysis of deviances in a Business Process Management scenario. We also discuss a wide and comprehensive
experimental analysis of the proposed approach and of a state-of-the-art DDMdiscovery solution. The
experimental findings confirm the flexibility, reliability and effectiveness of the proposed deviance detection
approach, and the improvement gained over its previous version