A Comparative Study of Machine Learning Algorithms for Anomaly Detection
  in Industrial Environments: Performance and Environmental Impact
Álvaro Huertas-García Carlos Martí-González Rubén García Maezo Alejandro Echeverría Rey
In the context of Industry 4.0, the use of artificial intelligence (AI) and
machine learning for anomaly detection is being hampered by high computational
requirements and associated environmental effects. This study seeks to address
the demands of high-performance machine learning models with environmental
sustainability, contributing to the emerging discourse on 'Green AI.' An
extensive variety of machine learning algorithms, coupled with various
Multilayer Perceptron (MLP) configurations, were meticulously evaluated. Our
investigation encapsulated a comprehensive suite of evaluation metrics,
comprising Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score,
Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro.
Simultaneously, the environmental footprint of these models was gauged through
considerations of time duration, CO2 equivalent, and energy consumption during
the training, cross-validation, and inference phases. Traditional machine
learning algorithms, such as Decision Trees and Random Forests, demonstrate
robust efficiency and performance. However, superior outcomes were obtained
with optimised MLP configurations, albeit with a commensurate increase in
resource consumption. The study incorporated a multi-objective optimisation
approach, invoking Pareto optimality principles, to highlight the trade-offs
between a model's performance and its environmental impact. The insights
derived underscore the imperative of striking a balance between model
performance, complexity, and environmental implications, thus offering valuable
directions for future work in the development of environmentally conscious
machine learning models for industrial applications.