Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts
<|reference_start|>Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts: Process Outcome Prediction entails predicting a discrete property of an unfinished process instance from its partial trace. High-capacity outcome predictors discovered with ensemble and deep learning methods have been shown to achieve top accuracy performances, but they suffer from a lack of transparency. Aligning with recent efforts to learn inherently interpretable outcome predictors, we propose to train a sparse Mixture-of-Experts where both the ``gate'' and ``expert'' sub-nets are Logistic Regressors. This ensemble-like model is trained end-to-end while automatically selecting a subset of input features in each sub-net, as an alternative to the common approach of performing a global feature selection step prior to model training. Test results on benchmark logs confirmed the validity and efficacy of this approach.<|reference_end|>
@article{folino2024discussion:,
    title={Discussion: Effective and Interpretable Outcome Prediction by Training
  Sparse Mixtures of Linear Experts},
    author={Francesco Folino, Luigi Pontieri and Pietro Sabatino},
    journal={Proceedings of the 1st International Workshop on Explainable
  Knowledge Aware Process Intelligence, June 20--22, 2024, Roccella Jonica,
  Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13526},
    primaryClass={cs.LG}
}