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When is resampling beneficial for feature selection with imbalanced . . . Feature selection (FS) is commonly recommended for wide datasets We aim to find the best combination and order to apply FS and resampling 14 datasets, 5 classifiers, 7 FS, and 7 balancing strategies were tested The best configuration was SVM-RFE used before RUS for the SVM-G classifier
Feature ImBalance in Machine Learning | by Yoshimasa | Medium What is Feature Imbalance? Feature imbalance occurs when certain values in an independent variable appear much more frequently than others This can happen in both categorical and numerical
Debiased Recommendation with User Feature Balancing To efficiently balance the user distributions upon each item pair, we propose three strategies, including clipping, sampling, and adversarial learning to improve the training process For more robust optimization, we deploy an explicit model to capture the potential latent confounders in recommendation systems
Debiased Recommendation with User Feature Balancing Traditional models mostly rely on the inverse propensity score (IPS), which can be hard to estimate and may suffer from the high variance issue To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing
Improving Class Balancing at Both Feature Extractor and Classifier Head The feature-balancing head (with green background) is proposed to help the feature extractor more fairly learn from each class, and the class-division head (with yellow background) is proposed to directly alleviate the class imbalance issued by training a class-balanced classifier
Maintaining Feature Balance in Machine Learning Models One key aspect to consider when building machine learning models is the balance of column importance An overly dominant column may indicate an unbalanced model, potential data leakage, or a risk to the model's robustness in production settings
Improving taxonomic classification with feature space balancing In this article, we show that k -mer profiles are predictive features for taxonomic classification, and when used in combination with dataset balancing and simple ML models outperform DL methods
An Anchor-Free Method Based on Feature Balancing and Refinement Network . . . In this article, a novel detection method named feature balancing and refinement network (FBR-Net) is proposed First, our method eliminates the effect of anchors by adopting a general anchor-free strategy that directly learns the encoded bounding boxes
How to Handle Imbalanced Classes in Feature Engineering Learn some of the best techniques for handling imbalanced classes in feature engineering, such as sampling, weighting, feature selection, feature extraction, and feature generation