A novel SMOTE-based resampling technique trough noise detection and the boosting procedure

Sağlam, Fatih; Ali, Mehmet

doi:10.1016/j.eswa.2022.117023

Cited by 41 publications

(10 citation statements)

References 43 publications

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“…Data class imbalance is a condition when the proportion of data is skewed to one class. This condition will greatly affect the formed model, especially when making predictions [11]. The problem of unbalanced data will be handled using the Synthetic Minority Over-sampling Technique (SMOTE) method.…”

Section: Research By Kuvvetlimentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Artificial Neural Network and Naive Bayes Classification Using Unbalanced Data Handling

Lestari,

Indahwati,

Erfiani

et al. 2023

BAREKENG: J. Math. & App.

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Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable. Therefore, unbalanced classification is a problem that must be taken seriously. This study will handle unbalanced data using the Synthetic Minority Over-Sampling Technique (SMOTE). The classification methods that are quite popular are the Naïve Bayes Classifier (NB) and the Resilient Backpropagation Artificial Neural Network (Rprop-ANN). The data used comes from the Health Nutrition Research and Development Agency (Balitbangkes) which consists of 2499 observations. This study examines the use of NB and ANN using the SMOTE method to classify the incidence of anemia in young women in Indonesia. Modeling is done on 80% of training data and predictions on 20% of test data. The analysis shows that SMOTE can perform better than not handling unbalanced data. Based on the results of the study, the best method for predicting the incidence of anemia is the Naïve Bayes method, with the sensitivity value of 82%.

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Section: Research By Kuvvetlimentioning

confidence: 99%

“…Unbalanced data is a condition where the distribution of data classes has different proportions. There are more response data classes (majority class) than other data classes [11]. One method to overcome the problem of unbalanced response data is the Synthetic Minority Over-Sampling Technique (SMOTE).…”

Section: Smotementioning

confidence: 99%

A Comparison of Artificial Neural Network and Naive Bayes Classification Using Unbalanced Data Handling

Lestari,

Indahwati,

Erfiani

et al. 2023

BAREKENG: J. Math. & App.

View full text Add to dashboard Cite

show abstract

“…Evolutionary algorithms were also used to create a boosting-based algorithm [36]. SMOTEWB [37] is another boosting ensemble, which combines SMOTE with a noise detection method, into a boosting framework. Some methods have used hybrid approaches involving both boosting and bagging, such as EasyEnsemble and BalanceCascade [38].…”

Section: Ensemble Learning In Imbalanced Classificationmentioning

confidence: 99%

Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning

Grina

Elouedi

Lefèvre

2023

International Journal of Approximate Reasoning

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“…Evolutionary algorithms were also used to create a boosting-based algorithm [19]. SMOTEWB [29] is another boosting ensemble, which combines SMOTE with a noise detection method, into a boosting framework.…”

Section: Ensemble Learning In Imbalanced Classificationmentioning

confidence: 99%

Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble

Grina

Elouedi

Lefèvre

2022

Lecture Notes in Computer Science

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In many real-world binary classification problems, one class tends to be heavily underrepresented when it consists of far fewer observations than the other class. This results in creating a biased model with undesirable performance. Different techniques, such as undersampling, have been proposed to fix this issue. Ensemble methods have also been proven to be a good strategy to improve the performance of the resulting model in the case of class imbalance. In this paper, we propose an evidential undersampling-based ensemble approach. To alleviate the issue of losing important data, our undersampling technique assigns soft evidential labels to each majority instance, which are later used to discard only the unwanted observations, such as noisy and ambiguous examples. Finally, to improve the final results, the proposed undersampling approach is incorporated into an evidential classifier fusion-based ensemble. The comparative study against wellknown ensemble methods reveal that our method is efficient according to the G-Mean and F-Score measures.

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A novel SMOTE-based resampling technique trough noise detection and the boosting procedure

Cited by 41 publications

References 43 publications

A Comparison of Artificial Neural Network and Naive Bayes Classification Using Unbalanced Data Handling

A Comparison of Artificial Neural Network and Naive Bayes Classification Using Unbalanced Data Handling

Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning

Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble

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