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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2020 The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method.
Windyaning Ustyannie, Suprapto Suprapto
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Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets [PDF]
, 2009 A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g.
Bermejo, P. +4 more
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Using deep learning for trajectory classification in imbalanced dataset
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Deep learning has gained much popularity in the past years due to GPU advancements, cloud computing improvements, and its supremacy, considering the accuracy results when trained on massive datasets.
Nicksson Ckayo Arrais de Freitas +3 more
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Classification Problem in Imbalanced Datasets
, 2020 La classification est une tâche d'exploration de données. Elle vise à extraire des connaissances à partir de grands ensembles de données. Il existe deux types de classification. La première est connue sous le nom de classification complète, et elle est appliquée à des ensembles de données équilibrés. Cependant, lorsqu'elle est appliquée à des ensembles
Aouatef, Mahani,, Baba, Ali, Ahmed Riad
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Is Diabetic Retinopathy Grading Biased by Imbalanced Datasets?
, 2022 Diabetic retinopathy (DR) is one of the most severe complications of diabetes and the leading cause of vision loss and even blindness. Retinal screening contributes to early detection and treatment of diabetic retinopathy. This eye disease has five stages, namely normal, mild, moderate, severe and proliferative diabetic retinopathy.
Monteiro, Fernando C., Rufino, José
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Asymmetric gradient penalty based on power exponential function for imbalanced data classification
Complex & Intelligent Systems, 2023 Model bias is a tricky problem in imbalanced data classification. An asymmetric gradient penalty method is proposed based on the power exponential function to alleviate this. The methodology integrates a power exponential function as a moderator into the
Linyong Zhou +3 more
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Multi-class pattern classification in imbalanced data [PDF]
, 2010 The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training ...
Ghanem, Amal S. +2 more
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Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
Applied Sciences, 2020 In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring.
Adolfo Rangel-Díaz-de-la-Vega +4 more
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Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data [PDF]
Journal of Universal Computer Science, 2020 This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data.
Oscar Camacho-Nieto +2 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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