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Advancing broad learning through structured feature generation
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DOI：10.1016/j.eswa.2025.129948.png)
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Abstract
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• Tackles randomness redundancy in broad learning systems with structured design. • Improves accuracy compared to classic broad learning systems in classification and regression tasks. • Addresses the lack of robustness in random neural architectures when faced with noise and scarce data. • Structured network design produces interpretable feature maps, enabling transparent AI decisions.
Keywords:
Broad learning system
Randomized neural networks
Random features
Feature generation
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