Importance-SMOTE: a synthetic minority oversampling method for noisy imbalanced data

Liu, Jie

doi:10.1007/s00500-021-06532-4

Cited by 22 publications

(2 citation statements)

References 39 publications

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“…For this reason, the class imbalance problem has been fixed with Synthetic Minority Over-sampling Technique (SMOTE). By learning from the original data, the sample numbers in the groups were equalized with SMOTE [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study

Akbulut

Yağın

Sahin

et al. 2023

JCM

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Background: In liver transplant (LT) recipients, immunosuppressive therapy may potentially increase the risk of severe COVID-19 and may increase the mortality in patients. However, studies have shown conflicting results, with various studies reporting poor outcomes while the others show no difference between the LT recipients and healthy population. The aim of this study is to determine the impact of the COVID-19 pandemic on survival of LT recipients. Methods: This is a retrospective cohort study analyzing the data from 387 LT recipients diagnosed with COVID-19. LT recipients were divided into two groups: survival (n = 359) and non-survival (n = 28) groups. A logistic regression model was used to determine the independent risk factors for mortality. Machine learning models were used to analyze the contribution of independent variables to the mortality in LT recipients. Results: The COVID-19-related mortality rate in LT recipients was 7.2%. Multivariate analysis showed that everolimus use (p = 0.012; OR = 6.2), need for intubation (p = 0.001; OR = 38.4) and discontinuation of immunosuppressive therapy (p = 0.047; OR = 7.3) were independent risk factors for mortality. Furthermore, COVID-19 vaccination reduced the risk of mortality by 100 fold and was the single independent factor determining the survival of the LT recipients. Conclusion: The effect of COVID-19 infection on LT recipients is slightly different from the effect of the disease on the general population. The COVID-19-related mortality is lower than the general population and vaccination for COVID-19 significantly reduces the risk of mortality.

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Section: Methodsmentioning

confidence: 99%

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study

Akbulut

Yağın

Sahin

et al. 2023

JCM

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“…For example, imbalanced data related to anomaly detection [1], fault diagnosis kesalahan [2,3], disease diagnosis [4,5], or facial recognition [6]. Imbalanced data [7] means that in the classification process involving two classes, the number of samples for a particular class is much larger than the samples number for other classes [8,9]. Usually, the class that has the larger number of samples is called the majority class, and the other class is called the minority.…”

Section: Introductionmentioning

confidence: 99%

The Application of Repeated SMOTE for Multi Class Classification on Imbalanced Data

Rachmatullah

2022

matrik

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One of the problems that are often faced by classifier algorithms is related to the problem of imbalanced data. One of the recommended improvement methods at the data level is to balance the number of data in different classes by enlarging the sample to the minority class (oversampling), one of which is called The Synthetic Minority Oversampling Technique (SMOTE). SMOTE is commonly used to balance data consisting of two classes. In this research, SMOTE was used to balance multi-class data. The purpose of this research is to balance multi-class data by applying SMOTE repeatedly. This iterative process needs to be applied if the number of unbalanced data classes is more than two classes, because the one-time SMOTE process is only suitable for binary classification or the number of unbalanced data classes is only one class. To see the performance of iterative SMOTE, the SMOTE datasets were classified using a neural network, k-NN, Nave Bayes, and Random Forest and the performance measures were measured in terms of accuracy, sensitivity, and specificity. The experiment in this research used the Glass Identification dataset which had six classes, and the SMOTE process was repeated five times. The best performance was achieved by the Random Forest classifier method with accuracy = 86.27%, sensitivity = 86.18%, and specificity = 95.82%. The result of experiment present that repeated SMOTE results can increase the performance of classification.

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

Thendral,

Abinaya,

Devi

2024

Neural Comput & Applic

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Importance-SMOTE: a synthetic minority oversampling method for noisy imbalanced data

Cited by 22 publications

References 39 publications

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study

The Application of Repeated SMOTE for Multi Class Classification on Imbalanced Data

Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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