Results 11 to 20 of about 219,349 (181)
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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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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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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Machine learning for stroke prediction using imbalanced data [PDF]
Scientific ReportsThe research focused on predicting strokes, a significant threat to health and well-being. The primary challenge addressed was the use of a highly imbalanced dataset.
Nataliia Melnykova +5 more
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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
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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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A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models
Axioms, 2022 Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine ...
Ming Zheng +5 more
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Imbalanced Data Classification Method Based on LSSASMOTE
IEEE Access, 2023 Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
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Bicriteria Oversampling for Imbalanced Data Classification
Procedia Computer Science, 2022 The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized.
Joanna Jedrzejowicz, Piotr Jedrzejowicz
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