Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts

• URL: http://arxiv.org/abs/2407.13526v1
• Date: Thu, 18 Jul 2024 13:59:10 GMT
• Title: Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts
• Authors: Francesco Folino, Luigi Pontieri, Pietro Sabatino,
• Abstract summary: 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.
• Score: 4.178382980763478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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