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- W4313332016 abstract "Machine learning algorithms are valuable tools for solving a wide variety of complex engineering problems. Usually, those problems have multiple criteria to fulfill, but such machine learning-based solutions are usually optimized using a single criterion. In such instances, a multi-objective optimization-based approach could bring interesting solutions by determining a set of Pareto-optimal solutions with different trade-off. Therefore, a multi-criteria decision-making process must be carried out. To the authors’ present knowledge, multi-criteria decision-making is yet to be fully explored for selecting preferable Pareto-optimal machine learning models after the training step. Therefore, this paper proposes applying and comparing five different multi-criteria decision-making techniques for selecting a preferred machine learning model. Additionally, an ensemble-based framework is proposed to cope with the difficulty of selecting parameters for such techniques. Those tools are tested on a complex real-world drinking-water quality monitoring problem. Results based on the [Formula: see text] score indicate that via a multi-criteria decision-making process ([Formula: see text]), it is possible to select better solutions than single-criterion approaches ([Formula: see text]). Moreover, the proposed ensemble framework is able to mitigate the difficulty in defining preferences and regions of interest, achieving competitive solutions." @default.
- W4313332016 created "2023-01-06" @default.
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- W4313332016 date "2023-02-13" @default.
- W4313332016 modified "2023-10-16" @default.
- W4313332016 title "Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring Problem" @default.
- W4313332016 doi "https://doi.org/10.1142/s0219622023500104" @default.
- W4313332016 hasPublicationYear "2023" @default.
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