Results 61 to 70 of about 219,349 (181)
A Classification Method Based on Feature Selection for Imbalanced Data
IEEE Access, 2019 Imbalanced data are very common in the real world, and it may deteriorate the performance of the conventional classification algorithms. In order to resolve the imbalanced classification problems, we propose an ensemble classification method that ...
Yi Liu +4 more
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CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
, 2017 Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature.
Ahmed, Sajid +5 more
core +1 more source
Partial Resampling of Imbalanced Data
, 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 ...
Kamalov, Firuz +2 more
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Class prediction for high-dimensional class-imbalanced data
BMC Bioinformatics, 2010 Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional
Lusa Lara, Blagus Rok
doaj +1 more source
Understanding imbalanced data: XAI & interpretable ML framework
Machine LearningAbstractThere is a gap between current methods that explain deep learning models that work on imbalanced image data and the needs of the imbalanced learning community. Existing methods that explain imbalanced data are geared toward binary classification, single layer machine learning models and low dimensional data.
Dablain, Damien +4 more
openaire +3 more sources
Data Augmentation for Imbalanced Regression
, 2023 paper accepted at the AISTATS 2023 conference, to be published in PMLR (Proceedings of Machine Learning Research)
Stocksieker, Samuel +2 more
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CUS-RF-Based Credit Card Fraud Detection with Imbalanced Data
Journal of Risk Analysis and Crisis Response (JRACR), 2022 With the continuous expansion of the banks' credit card businesses, credit card fraud has become a serious threat to banking financial institutions. So, the automatic and real-time credit card fraud detection is the meaningful research work.
Wei Li, Cheng-shu Wu, Su-mei Ruan
doaj +1 more source
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
, 2016 Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.
Dai, Zhenwen +3 more
core
Extending Bagging for Imbalanced Data [PDF]
, 2013 Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside ...
Jerzy Błaszczyński +2 more
openaire +1 more source
Dual generative adversarial networks based on regression and neighbor characteristics.
PLoS ONEImbalanced data is a problem in that the number of samples in different categories or target value ranges varies greatly. Data imbalance imposes excellent challenges to machine learning and pattern recognition.
Weinan Jia +4 more
doaj +1 more source