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Found 7 papers, 3 papers with code
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics.
Extracting Process-Aware Decision Models from Object-Centric Process Data
Moreover, IODDA is able to discover which activities and object types are involved in the decision-making process.
Predicting student performance using sequence classification with time-based windows
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula.
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models
In this paper, we define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction.
Explainable artificial intelligence
Predictive Process Monitoring
Process Model Forecasting Using Time Series Analysis of Event Sequence Data
To this end, we develop a technique to forecast the entire process model from historical event data.
Conformance Checking of Mixed-paradigm Process Models
They are specifically interesting for process mining because they allow capturing complex behaviour in a compact way.
Predictive Process Model Monitoring using Recurrent Neural Networks
To achieve this, Processes-As-Movies (PAM) is introduced, i. e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution.
