Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts
This work addresses the need for transparent outcome prediction in process mining, though it is incremental in combining existing interpretable methods.
The paper tackles the problem of predicting outcomes in unfinished processes using interpretable models, achieving competitive accuracy with 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.