Abstract
Providing clean and safe drinking water is a crucial task for any water supply company. In such an activity, automatic anomaly detection plays a critical role in drinking water quality monitoring. Recent anomaly detection techniques use tools from the machine learning area, where after a supervised learning process, a given machine learning representation can classify an event as normal or as an anomaly. Most of the times this learning process is performed via optimisation with a single objective cost function. Nevertheless, a trade-off between false positives and false negatives in the classifying task must be achieved. For this task, multi-objective optimisation could be an interesting tool. In this exploratory work, it is used the simplest classifier machine and logistic regression, and it is explored the advantages of using a multi-objective optimisation approach for its training.
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Reynoso-Meza, G., Carreño-Alvarado, E.P. (2022). Multi-objective Logistic Regression for Anomaly Detection in Water Distribution Systems. In: Rocha, Á., López-López, P.C., Salgado-Guerrero, J.P. (eds) Communication, Smart Technologies and Innovation for Society . Smart Innovation, Systems and Technologies, vol 252. Springer, Singapore. https://doi.org/10.1007/978-981-16-4126-8_13
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DOI: https://doi.org/10.1007/978-981-16-4126-8_13
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