Tag: Feature Engineering
All the articles with the tag "Feature Engineering".
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Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints
This paper proposes a novel method combining contrastive learning with conditional variational autoencoders and mutual information constraints to extract style features from unlabeled data, demonstrating effectiveness on simple datasets like MNIST while facing challenges with natural image datasets due to augmentation limitations and qualitative evaluation.
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Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation
This paper introduces sparse-group boosting and a simulation-based group balancing algorithm within the 'sgboost' R package to mitigate variable selection bias in high-dimensional grouped data, demonstrating improved fairness and interpretability through simulations and ecological data analysis.