Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts
| Autor: | Folino, Francesco, Pontieri, Luigi, Sabatino, Pietro |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proceedings of the 1st International Workshop on Explainable Knowledge Aware Process Intelligence, June 20--22, 2024, Roccella Jonica, Italy |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: This paper summarizes results presented at workshop \emph{ML4PM 2023}, associated with conference ICPM 2023, October 23-27, 2023, Rome, Italy. 6 pages, 1 figure |
| Databáze: | arXiv |
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