Results 1 to 10 of about 90,025 (264)
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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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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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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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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Survey of Imbalanced Data Methodologies
CoRR, 2021 7 pages, 4 ...
Lian Yu, Nengfeng Zhou
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Framework for imbalanced data classification
Procedia Computer Science, 2021 Classifying imbalanced data remains a challenging task. The paper presents a framework for imbalanced datasets classification which makes use of different methods of oversampling and methods of dynamical selection of classifiers. The framework allows to perform extensive experiments to determine best possible configuration for the examined dataset in ...
Mikołaj Błaszczyk +1 more
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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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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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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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