Results 1 to 10 of about 224,004 (328)
Binary Classification with Imbalanced Data [PDF]
Entropy, 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 ...
Jyun-You Chiang +4 more
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Deep Over-sampling Framework for Classifying Imbalanced Data [PDF]
, 2017 Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured
B Krawczyk +15 more
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Spectral Clustering with Imbalanced Data [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013 Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present. We show that Ratio-Cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced data since they tend
Qian, Jing, Saligrama, Venkatesh
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Resampling imbalanced data for network intrusion detection datasets [PDF]
Journal of Big Data, 2021 Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
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Imbalanced data classification using graph based transformation [PDF]
Scientific ReportsImbalanced data classification is a challenging task in real applications. In this work. A method is proposed for image classification using imbalanced distribution of classes.
Maryam Imani
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Multi-class Boosting for imbalanced data. [PDF]
, 2015 We consider the problem of multi-class classification with imbalanced data-sets. To this end, we introduce a cost-sensitive multi-class Boosting algorithm (BAdaCost) based on a generalization of the Boosting margin, termed multi-class cost-sensitive ...
Baumela Molina, Luis +2 more
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Classification performance assessment for imbalanced multiclass data [PDF]
Scientific ReportsThe evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality solutions, especially given the pronounced context-sensitivity of certain systems, particularly in fields such as biomedicine.
Jesús S. Aguilar-Ruiz, Marcin Michalak
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Classification Algorithm for Structured Imbalanced Data Based on Convolutional Neural Network [PDF]
Jisuanji gongcheng, 2023 Convolutional Neural Network(CNN) are widely used in image processing, object tracking, natural language, and other fields because of their efficient feature extraction capabilities and their use of fewer parameters.To address the problem in which ...
XU Hong, JIAO Guie, ZHANG Wenjun, CHEN Yimin
doaj +1 more source
An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data
Mathematics, 2023 This article proposes a new AdaBoost method with k′k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k′k-means Bayes method and then deals with the imbalanced classification problem using multiple ...
Yanfeng Zhang, Lichun Wang
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Selective oversampling approach for strongly imbalanced data [PDF]
PeerJ Computer Science, 2021 Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling.
Peter Gnip +2 more
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