A double ensemble that fuses features from pretrained CNNs and ViTs and ensembles tuned ML classifiers reaches 97.5% to 99.3% accuracy on three public brain MRI datasets, but the gains are not benchmarked against a held-out SOTA baseline.
Hyperparameter Search in Machine Learning
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abstract
We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based on given data. Most common learning algorithms feature a set of hyperparameters that must be determined before training commences. The choice of hyperparameters can significantly affect the resulting model's performance, but determining good values can be complex; hence a disciplined, theoretically sound search strategy is essential.
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Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification
A double ensemble that fuses features from pretrained CNNs and ViTs and ensembles tuned ML classifiers reaches 97.5% to 99.3% accuracy on three public brain MRI datasets, but the gains are not benchmarked against a held-out SOTA baseline.