Explainable Machine Learning for Alarm Prediction

Kalleb Abreu, Julio Reis, André Santos, Giorgio Zucchi

2024

Abstract

This paper evaluates machine learning models for the prediction of alarms using geographical clustering, exploring data from an Italian company. The models encompass a spectrum of algorithms, including Naive Bayes (NB), XGBoost (XGB), and Multilayer Perceptron (MLP), coupled with encoding techniques, and clustering methodologies, namely COOP (Coopservice) and KPP (K-Means++). The XGB models emerge as the most effective, yielding the highest AP (Average Precision) values across models based on MLP and NB. Hyperparameter tuning for XGB models reveals default values perform well. Our model explainability analyses reveal the significant impact of geographical location (cluster) and the time interval when the predictions are made. Challenges arise in handling dataset imbalances, impacting minority alarm class predictions. the insights gained from this study lay the groundwork for future investigations in the field of geographical alarm prediction. The identified challenges, such as imbalanced datasets, offer opportunities for refining methodologies. As we move forward, a deeper exploration of one-class algorithms holds promise for addressing these challenges and enhancing the robustness of predictive models in similar contexts.

Paper Citation

in Harvard Style

Abreu K., Reis J., Santos A. and Zucchi G. (2024). Explainable Machine Learning for Alarm Prediction. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 690-697. DOI: 10.5220/0012625000003690

in Bibtex Style

@conference{iceis24,
author={Kalleb Abreu and Julio Reis and André Santos and Giorgio Zucchi},
title={Explainable Machine Learning for Alarm Prediction},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={690-697},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012625000003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Explainable Machine Learning for Alarm Prediction
SN - 978-989-758-692-7
AU - Abreu K.
AU - Reis J.
AU - Santos A.
AU - Zucchi G.
PY - 2024
SP - 690
EP - 697
DO - 10.5220/0012625000003690
PB - SciTePress