Results 1 to 10 of about 222,810 (285)
Binary Classification with Imbalanced Data. [PDF]
Entropy (Basel), 2023 When the binary response variable contains an excess of zero counts, the data are imbalanced. Imbalanced data cause trouble for binary classification. To simplify the numerical computation to obtain the maximum likelihood estimators of the zero-inflated Bernoulli (ZIBer) model parameters with imbalanced data, an expectation-maximization (EM) algorithm ...
Chiang JY +4 more
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Spectral clustering with imbalanced data [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $ε$-graphs. This is because the objectives such as Ratio-Cut
Jing Qian, Venkatesh Saligrama
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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. We propose two machine learning algorithms to handle highly imbalanced classification problems.
Siong Thye Goh, Cynthia Rudin
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Interpretable ML for Imbalanced Data
CoRR, 2022 Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear.
Damien A. Dablain +4 more
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Partial Resampling of Imbalanced Data
CoRR, 2022 Imbalanced data is a frequently encountered problem in machine learning. Despite a vast amount of literature on sampling techniques for imbalanced data, there is a limited number of studies that address the issue of the optimal sampling ratio. In this paper, we attempt to fill the gap in the literature by conducting a large scale study of the effects ...
Firuz Kamalov +2 more
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Survey of Imbalanced Data Methodologies
CoRR, 2021 7 pages, 4 ...
Lian Yu, Nengfeng Zhou
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An Imbalanced Data Rule Learner [PDF]
, 2005 Imbalanced data learning has recently begun to receive much attention from research and industrial communities as traditional machine learners no longer give satisfactory results. Solutions to the problem generally attempt to adapt standard learners to the imbalanced data setting.
Canh Hao Nguyen, Tu Bao Ho
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Mine Classification With Imbalanced Data [PDF]
IEEE Geoscience and Remote Sensing Letters, 2009 In many remote-sensing classification problems, the number of targets (e.g., mines) present is very small compared with the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly.
David P. Williams +2 more
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Data Augmentation for Imbalanced Regression
CoRR, 2023 paper accepted at the AISTATS 2023 conference, to be published in PMLR (Proceedings of Machine Learning Research)
Samuel Stocksieker +2 more
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Oversampling Techniques for Imbalanced Data in Regression
Expert Systems with Applications, 2023 Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (
Samir Brahim Belhaouari +4 more
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