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• W4313889431 startingPage "906" @default.
• W4313889431 abstract "In recent years, feature selection has emerged as a major challenge in machine learning. In this paper, considering the promising performance of metaheuristics on different types of applications, six physics-inspired metaphor algorithms are employed for this problem. To evaluate the capability of dimensionality reduction in these algorithms, six diverse-natured datasets are used. The performance is compared in terms of the average number of features selected (AFS), accuracy, fitness, convergence capabilities, and computational cost. It is found through experiments that the accuracy and fitness of the Equilibrium Optimizer (EO) are comparatively better than the others. Finally, the average rank from the perspective of average fitness, average accuracy, and AFS shows that EO outperforms all other algorithms." @default.
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• W4313889431 date "2023-01-09" @default.
• W4313889431 modified "2023-10-14" @default.
• W4313889431 title "Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor" @default.
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• W4313889431 doi "https://doi.org/10.3390/app13020906" @default.
• W4313889431 hasPublicationYear "2023" @default.
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