Skip to content

The implementation of the paper: "Explainable Artificial Intelligence for Improved Modeling of Processes".

License

Notifications You must be signed in to change notification settings

rizavelioglu/ml4prom

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ML4ProM

Check out the paper on: arXiv

Please follow the notebooks to reproduce results:

How to train models and output results?

Inside the project directory (../ml4prom/) execute following to get to know more about the args:

python -m src.models.train_model -h

which returns:

  --debug DEBUG         When True, plots ROC-Curve & Confusion Matrix
  --seq_encoding SEQ_ENCODING
                        Possible encodings; 'one-hot' & 'n-gram' where n is an integer
  --unique_traces UNIQUE_TRACES
                        when True, duplicate traces(trace variants) are removed from dataset
  --remove_biased_feats REMOVE_BIASED_FEATS
                        when True, the biased features are removed from dataset, e.g. patient is dead in COVID dataset

The following command does multiple things:

  • load all datasets
  • apply preprocessing, e.g. remove biased features, remove duplicate traces, etc.
  • encode traces (sequence of events)
  • train ML models with StratifiedKFold cross-validation
  • output a .csv file to ./reports/ including the accuracy scores
python -m src.models.train_model --seq_encoding one-hot --remove_biased_feats --unique_traces

Citation:

@inproceedings{velioglu2022explainable,
  title={Explainable Artificial Intelligence for Improved Modeling of Processes},
  author={Velioglu, Riza and G{\"o}pfert, Jan Philip and Artelt, Andr{\'e} and Hammer, Barbara},
  booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
  pages={313--325},
  year={2022},
  organization={Springer}
}

About

The implementation of the paper: "Explainable Artificial Intelligence for Improved Modeling of Processes".

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published