Results 11 to 20 of about 141,868 (281)
Box Drawings for Learning with Imbalanced Data [PDF]
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes.
Abe N. +4 more
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Distribution-sensitive learning for imbalanced datasets [PDF]
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013 Many real-world face and gesture datasets are by nature imbalanced across classes. Conventional statistical learning models (e.g., SVM, HMM, CRY), however, are sensitive to imbalanced datasets.
Davis, Randall +2 more
core +5 more sources
Fairness-aware Class Imbalanced Learning [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the ...
Subramanian, Shivashankar +4 more
openaire +3 more sources
CoRR, 2022 Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when the data is imbalanced. Since many real-world datasets are naturally imbalanced, and improper handling of this issue can lead to quite useless models, this issue should be ...
Singh, Prabhant, Vanschoren, Joaquin
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Deep reinforcement learning for imbalanced classification [PDF]
Applied Intelligence, 2020 Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced.
Enlu Lin, Qiong Chen, Xiaoming Qi
openaire +2 more sources
Imbalanced Class Learning in Epigenetics [PDF]
Journal of Computational Biology, 2014 In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class ...
Haque, M. Muksitul +2 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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ROSE: a Package for Binary Imbalanced Learning [PDF]
The R Journal, 2014 The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class.
Nicola Lunardon +2 more
openaire +5 more sources
Efficient Augmentation for Imbalanced Deep Learning
2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 Deep learning models tend to memorize training data, which hurts their ability to generalize to under-represented classes. We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is ...
Damien Dablain +3 more
openaire +2 more sources
Feature Analysis for Imbalanced Learning
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2020 Based on the results of artificial samples generated in the minority class and through the label regulation of the neighbor samples of the majority class, the precision of the classification prediction for imbalanced learning has clearly been enhanced.
Dao Nam Anh +3 more
openaire +1 more source