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Exploiting Modular Redundancy for approximating Random Forest classifiers
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DOI：10.1016/j.future.2025.108330.png)
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Abstract
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• A modular redundancy-based approximation is proposed for decision tree ensembles. • Modular redundancy is used to select only a subset of trees for classifying each class label. • This strategy allows aggressive approximation while preserving accuracy. • The effectiveness of the solution is demonstrated and shown.
Keywords:
Random Forest
Approximate Computing
Edge Computing
Artificial Intelligence
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