Results 11 to 20 of about 222,810 (285)
Imbalanced data classification using graph based transformation. [PDF]
Sci RepImbalanced data classification is a challenging task in real applications. In this work. A method is proposed for image classification using imbalanced distribution of classes.
Imani M.
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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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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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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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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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Multicriteria Classifier Ensemble Learning for Imbalanced Data
IEEE Access, 2022 One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that ...
Weronika Wegier +2 more
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A method for increasing accuracy of credit imbalanced data [PDF]
فصلنامه بورس اوراق بهادارThe main goal of this research is to provide a method that can be used to increase the accuracy of credit imbalance data. Financial fraud is a fundamental problem that affects both the financial sector and life and plays an important role in affecting ...
Arash GhorbanniaDelavar +1 more
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Hellinger Distance Trees for Imbalanced Streams [PDF]
, 2014 Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem.
Brooke, J. M. +3 more
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A Novel Imbalanced Ensemble Learning in Software Defect Predication
IEEE Access, 2021 With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the
Jianming Zheng +4 more
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