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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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Imbalanced SVM‐Based Anomaly Detection Algorithm for Imbalanced Training Datasets [PDF]
ETRI Journal, 2017 Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples.
GuiPing Wang, JianXi Yang, Ren Li
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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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Impact of imbalanced features on large datasets
Frontiers in Big DataThe exponential growth of image and video data motivates the need for practical real-time content-based searching algorithms. Features play a vital role in identifying objects within images.
Waleed Albattah, Rehan Ullah Khan
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LoRAS: an oversampling approach for imbalanced datasets [PDF]
Machine Learning, 2020 AbstractThe Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model.
Saptarshi Bej +4 more
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A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets
IEEE Access, 2021 Labelled imbalanced data, used for classification problems, have an unequal distribution of samples over the classes. Traditional classification models, such as random forest, gradient boosting, face a problem when dealing with imbalanced datasets.
Saptarshi Bej +4 more
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Adaptive Age Estimation towards Imbalanced Datasets
Applied Sciences, 2023 Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category.
Zhiang Dong, Xiaoqiang Li
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Probability-Based Synthetic Minority Oversampling Technique
IEEE Access, 2023 Many real-life datasets suffer from class imbalance, where one or more classes are under-represented in the dataset, resulting in reduced classifier performance, with the expected decline in quality of procedures depending on the classification results ...
Najwa Altwaijry
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IEEE Access, 2022 The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others.
Abeer S. Desuky +4 more
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Over-sampling imbalanced datasets using the Covariance Matrix [PDF]
EAI Endorsed Transactions on Energy Web, 2020 INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority ...
Ireimis Leguen-deVarona +3 more
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