An Asymmetric Contrastive Loss for Handling Imbalanced Datasets [PDF]
Entropy, 2022 Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space.
Valentino Vito, Lim Yohanes Stefanus
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Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets. [PDF]
Front Artif IntellIntroduction Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and ...
Abousaber I, Abdallah HF, El-Ghaish H.
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The receiver operating characteristic curve accurately assesses imbalanced datasets. [PDF]
Patterns (N Y)Summary Many problems in biology require looking for a “needle in a haystack,” corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance.
Richardson E +5 more
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A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems. [PDF]
Front Digit HealthThere has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods ...
Yang Y, Khorshidi HA, Aickelin U.
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Practical guide to building machine learning-based clinical prediction models using imbalanced datasets. [PDF]
Trauma Surg Acute Care OpenClinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations.
Luu J +6 more
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Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms. [PDF]
Appl Nanosci, 2023 One of the prominent uses of Predictive Analytics is Health care for more accurate predictions based on proper analysis of cumulative datasets. Often times the datasets are quite imbalanced and sampling techniques like Synthetic Minority Oversampling ...
Sowjanya AM, Mrudula O.
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Synthetic Boosted Resampling Using Deep Generative Adversarial Networks: A Novel Approach to Improve Cancer Prediction from Imbalanced Datasets. [PDF]
Cancers (Basel)Simple Summary This study explores various resampling methods and classifiers for imbalanced datasets, focusing on cancer diagnosis and prognosis. Traditional methods like SMOTE and ADASYN were replaced by GANs, which generate high-quality synthetic data
Gurcan F, Soylu A.
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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. [PDF]
PLoS One, 2015 Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots.
Saito T, Rehmsmeier M.
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An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal. [PDF]
Comput Intell Neurosci, 2022 Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions.
Ullah H +11 more
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A Boundary-Information-Based Oversampling Approach to Improve Learning Performance for Imbalanced Datasets [PDF]
Entropy, 2022 Oversampling is the most popular data preprocessing technique. It makes traditional classifiers available for learning from imbalanced data. Through an overall review of oversampling techniques (oversamplers), we find that some of them can be regarded as
Der-Chiang Li +3 more
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