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

TL;DR

This paper introduces a sparse Mixture-of-Experts model with logistic regressors for outcome prediction, achieving high accuracy while maintaining interpretability by automatically selecting relevant features.

Contribution

It proposes a novel end-to-end trainable sparse Mixture-of-Experts model with logistic regressors that enhances interpretability and feature selection in outcome prediction.

Findings

Validated on benchmark logs, demonstrating effectiveness.

Achieved high accuracy with interpretable models.

Automatically selects relevant features per expert.

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.