# Contextual Mixture of Experts: Integrating Knowledge into Predictive Modeling

arXiv: https://arxiv.org/abs/2211.00558 · HTML: https://mlsanity.com/paper/2211.00558/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts](https://mlsanity.com/paper/1907.06994/) (2019)

## Built on by (most cited first, 1 of 1)

- [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025)

## Abstract (arXiv)

This work proposes a new data-driven model devised to integrate process knowledge into its structure to increase the human-machine synergy in the process industry. The proposed Contextual Mixture of Experts (cMoE) explicitly uses process knowledge along the model learning stage to mold the historical data to represent operators' context related to the process through possibility distributions. This model was evaluated in two real case studies for quality prediction, including a sulfur recovery unit and a polymerization process. The contextual mixture of experts was employed to represent different contexts in both experiments. The results indicate that integrating process knowledge has increased predictive performance while improving interpretability by providing insights into the variables affecting the process's different regimes.