Results 11 to 20 of about 39,918 (213)
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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Learning Imbalanced Datasets With Maximum Margin Loss
2021 IEEE International Conference on Image Processing (ICIP), 2021 A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization.
Kang, Haeyong, Vu, Thang, Yoo, Chang D.
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Conversion of adverse data corpus to shrewd output using sampling metrics
Visual Computing for Industry, Biomedicine, and Art, 2020 An imbalanced dataset is commonly found in at least one class, which are typically exceeded by the other ones. A machine learning algorithm (classifier) trained with an imbalanced dataset predicts the majority class (frequently occurring) more than the ...
Shahzad Ashraf +4 more
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Active Learning for Imbalanced Datasets
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020 Active learning increases the effectiveness of labeling when only subsets of unlabeled datasets can be processed manually. To our knowledge, existing algorithms are designed under the assumption that datasets are balanced. However, many real-life datasets are actually imbalanced and we propose two adaptations of active learning to tackle imbalance ...
Aggarwal, Umang +2 more
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Active Class Incremental Learning for Imbalanced Datasets [PDF]
, 2020 Accepted in IPCV workshop from ...
Eden Belouadah +3 more
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SSMFN: a fused spatial and sequential deep learning model for methylation site prediction [PDF]
PeerJ Computer Science, 2021 Background Conventional in vivo methods for post-translational modification site prediction such as spectrophotometry, Western blotting, and chromatin immune precipitation can be very expensive and time-consuming.
Favorisen Rosyking Lumbanraja +4 more
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IDPP: Imbalanced Datasets Pipelines in Pyrus
, 2023 We showcase and demonstrate IDPP, a Pyrus-based tool that offers a collection of pipelines for the analysis of imbalanced datasets. Like Pyrus, IDPP is a web-based, low-code/no-code graphical modelling environment for ML and data analytics applications. On a case study from the medical domain, we solve the challenge of re-using AI/ML models that do not
Amandeep Singh, Olga Minguett
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Multilayer Feedforward Neural Network for Internet Traffic Classification.
International Journal of Interactive Multimedia and Artificial Intelligence, 2020 Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows ...
N. Manju, B. S. Harish, N. Nagadarshan
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Federated Learning on Clinical Benchmark Data: Performance Assessment
Journal of Medical Internet Research, 2020 BackgroundFederated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy ...
Lee, Geun Hyeong, Shin, Soo-Yong
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Bayes classifiers for imbalanced traffic accidents datasets [PDF]
Accident Analysis & Prevention, 2016 Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the ...
Mujalli, R +2 more
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