Abstract
In the areas of machine-learning/big data, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial, however, throughout the years various ways to counter this optimization problem have been presented. This work presents how feature-selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems.
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Notes
- 1.
Pareto optimality can be roughly defined as a state at which resources in a given system are optimized such that one dimension cannot improve without a second worsening. “The main idea (...) is that a society is enjoying maximum ophelimity when no one can be made better off without making someone else worse off” [7].
- 2.
Involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial-and-error methods (in Merriam-Webster Dictionary).
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Acknowledgments
This article is a result of the project “Criação de um Núcleo de I&D para a geração de novo conhecimento nas áreas de Inteligência Artificial, Machine Learning, Intelligent Marketing e One-2-One Marketing”, supported by Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).
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Coelho, D., Madureira, A., Pereira, I., Gonçalves, R. (2022). A Review on MOEA and Metaheuristics for Feature-Selection. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_21
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