Search Results for author: Jochen De Weerdt

Found 12 papers, 6 papers with code

Timing Process Interventions with Causal Inference and Reinforcement Learning

no code implementations7 Jun 2023 Hans Weytjens, Wouter Verbeke, Jochen De Weerdt

Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results.

Causal Inference reinforcement-learning +1

Can recurrent neural networks learn process model structure?

1 code implementation13 Dec 2022 Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt

In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log.

Predictive Process Monitoring

Predicting student performance using sequence classification with time-based windows

no code implementations16 Aug 2022 Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre, Richard Weber, Jochen De Weerdt

A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula.

Specificity

Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors

1 code implementation27 Jun 2022 Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt

For realising timely delivery, an accurate prediction of the remaining time of the delivery process is crucial.

Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring

1 code implementation13 Jun 2022 Hans Weytjens, Jochen De Weerdt

The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use.

Predictive Process Monitoring

Can deep neural networks learn process model structure? An assessment framework and analysis

1 code implementation24 Feb 2022 Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt

Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure.

Predictive Process Monitoring

Expert-driven Trace Clustering with Instance-level Constraints

no code implementations13 Oct 2021 Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt

Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups.

Clustering

Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring

no code implementations5 Jul 2021 Hans Weytjens, Jochen De Weerdt

Often the training and test sets are not completely separated, a data leakage problem particular to predictive process monitoring.

Predictive Process Monitoring

Process Outcome Prediction: CNN vs. LSTM (with Attention)

no code implementations14 Apr 2021 Hans Weytjens, Jochen De Weerdt

Attention is another technique that, in combination with LSTMs, has found application in time series classification and was included in our research.

General Classification Time Series +2

Predictive Process Model Monitoring using Recurrent Neural Networks

no code implementations5 Nov 2020 Johannes De Smedt, Jochen De Weerdt

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.

Predictive Process Monitoring Time Series Forecasting

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