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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.13526 |
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| _version_ | 1866910533704220672 |
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| author | Folino, Francesco Pontieri, Luigi Sabatino, Pietro |
| author_facet | Folino, Francesco Pontieri, Luigi Sabatino, Pietro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts Folino, Francesco Pontieri, Luigi Sabatino, Pietro Machine Learning 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. |
| title | Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.13526 |