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Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning
Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL.
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection
With the surge in data generation, both vertically (i. e., volume of data) and horizontally (i. e., dimensionality), the burden of the curse of dimensionality has become increasingly palpable.
Meta-learning approaches for few-shot learning: A survey of recent advances
Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction.
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning
Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models.
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs.
