RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification

Arafa, Ahmed; El-Fishawy, Nawal; Badawy, Mohammed; Radad, Marwa

doi:10.1016/j.jksuci.2022.06.005

Cited by 39 publications

(26 citation statements)

References 45 publications

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“…Recently, to overcome the limitations of SMOTE, new versions of SMOTE have been introduced. Therefore, the authors propose to use the new versions of SMOTE, e.g., A-SMOTE, RN-SMOTE, SMOTE-LOF, to deal with imbalances and compare them with the prior versions of SMOTE for further analysis [ 28 , 57 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

Hassanzadeh

Farhadian

Rafieemehr

2023

BMC Med Res Methodol

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Background Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the ICU. The main objective of the present study is to develop and evaluate SMOTE-based machine-learning tools for predicting hospital mortality in trauma patients with imbalanced data. Methods This retrospective cohort study was conducted on 126 trauma patients admitted to an intensive care unit at Besat hospital in Hamadan Province, western Iran, from March 2020 to March 2021. Data were extracted from the medical information records of patients. According to the imbalanced property of the data, SMOTE techniques, namely SMOTE, Borderline-SMOTE1, Borderline-SMOTE2, SMOTE-NC, and SVM-SMOTE, were used for primary preprocessing. Then, the Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) methods were used to predict patients' hospital mortality with traumatic injuries. The performance of the methods used was evaluated by sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), accuracy, Area Under the Curve (AUC), Geometric Mean (G-means), F1 score, and P-value of McNemar's test. Results Of the 126 patients admitted to an ICU, 117 (92.9%) survived and 9 (7.1%) died. The mean follow-up time from the date of trauma to the date of outcome was 3.98 ± 4.65 days. The performance of ML algorithms is not good with imbalanced data, whereas the performance of SMOTE-based ML algorithms is significantly improved. The mean area under the ROC curve (AUC) of all SMOTE-based models was more than 91%. F1-score and G-means before balancing the dataset were below 70% for all ML models except ANN. In contrast, F1-score and G-means for the balanced datasets reached more than 90% for all SMOTE-based models. Among all SMOTE-based ML methods, RF and ANN based on SMOTE and XGBoost based on SMOTE-NC achieved the highest value for all evaluation criteria. Conclusions This study has shown that SMOTE-based ML algorithms better predict outcomes in traumatic injuries than ML algorithms. They have the potential to assist ICU physicians in making clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

Hassanzadeh

Farhadian

Rafieemehr

2023

BMC Med Res Methodol

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“…RN-SMOTE is an approach for handling the class imbalance problem [ 24 ]. It first oversamples the imbalanced dataset using SMOTE by generating a set of synthetic samples from the minor class according to the equation below.…”

Section: Methodsmentioning

confidence: 99%

“…Many SMOTE extensions have been proposed to overcome these limitations. RN-SMOTE is one of the recent extensions that utilize the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to detect and remove noise after oversampling the imbalanced dataset through SMOTE [ 24 ]. Another extension is the Limiting Radius SMOTE (LR-SMOTE) which utilizes the Support Vector Machines (SVM) and k-means to remove the noise in the original data set then oversamples the data using SMOTE to generate new samples which are filtered based on the nearest neighbours [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

et al. 2023

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Background In the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets. These characteristics have a negative impact on the performance of most classifiers when used to classify cancer using genomic datasets. Results This paper introduces Reduced Noise-Autoencoder (RN-Autoencoder) for pre-processing imbalanced genomic datasets for precise cancer classification. Firstly, RN-Autoencoder solves the curse of dimensionality problem by utilizing the autoencoder for feature reduction and hence generating new extracted data with lower dimensionality. In the next stage, RN-Autoencoder introduces the extracted data to the well-known Reduced Noise-Synthesis Minority Over Sampling Technique (RN- SMOTE) that efficiently solve the problem of class imbalance in the extracted data. RN-Autoencoder has been evaluated using different classifiers and various imbalanced datasets with different imbalance ratios. The results proved that the performance of the classifiers has been improved with RN-Autoencoder and outperformed the performance with original data and extracted data with percentages based on the classifier, dataset and evaluation metric. Also, the performance of RN-Autoencoder has been compared to the performance of the current state of the art and resulted in an increase up to 18.017, 19.183, 18.58 and 8.87% in terms of test accuracy using colon, leukemia, Diffuse Large B-Cell Lymphoma (DLBCL) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. Conclusion RN-Autoencoder is a model for cancer classification using imbalanced gene expression datasets. It utilizes the autoencoder to reduce the high dimensionality of the gene expression datasets and then handles the class imbalance using RN-SMOTE. RN-Autoencoder has been evaluated using many different classifiers and many different imbalanced datasets. The performance of many classifiers has improved and some have succeeded in classifying cancer with 100% performance in terms of all used metrics. In addition, RN-Autoencoder outperformed many recent works using the same datasets.

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“…Soltanzadeh and Hashemzadeh 38 adopted an instance classification method to identify minority instances suitable for oversampling, and proposed the range‐controlled SMOTE method (RCSMOTE). Arafa et al 39 proposed a noise reduction SMOTE (RN‐SMOTE) method for handling imbalanced data. This method uses DBSCAN to eliminate the synthesized noise instances after oversampling with SMOTE, thereby improving the classification performance of the model.…”

Section: Relate Workmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

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One of the most widely used approaches to the class‐imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi‐armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi‐armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out‐of‐bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi‐armed bandit heterogeneous ensemble model is then assessed using 30 real‐world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state‐of‐the‐art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post‐hoc test.

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RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification

Cited by 39 publications

References 45 publications

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

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