Results 11 to 20 of about 103,585 (306)
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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Resampling imbalanced data for network intrusion detection datasets
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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Learning in imbalanced relational data
2008 19th International Conference on Pattern Recognition, 2008 Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes.
Amal Saleh Ghanem +2 more
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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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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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An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering, 2007 In classification problems, the class imbalance problem will cause a bias on the training of classifiers and will result in the lower sensitivity of detecting the minority class examples. The Mahalanobis-Taguchi System (MTS) is a diagnostic and forecasting technique for multivariate data.
Chao-Ton Su, Yu-Hsiang Hsiao
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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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