SMOTE-RkNN: A hybrid re-sampling method based on SMOTE and reverse k-nearest neighbors

Zhang, Aimin; Yu, Hualong; Huan, Zhangjun; Yang, Xibei; Zheng, Shang; Shang, Gao

doi:10.1016/j.ins.2022.02.038

Cited by 65 publications

(9 citation statements)

References 45 publications

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“…Level Class Dataset Avg. Acc MCMTD [18] Data Binary Number 94,47% SMOTE-RkNN [19] Data Binary Number 95% HybridDA [20] Data Binary Combine 96.73% CSMOUTE [21] Data Binary Number 95% MC-CCR [22] Data Multi Number 97,12% ROS-NCL Data Multi Text 97,94% algorithm. Consequently, the proposed hybrid sampling approaches have different characteristics while solving problems.…”

Section: Methodsmentioning

confidence: 99%

“…This method focuses on resolving imbalanced data problems in binary classification, generating virtual samples for the minority class, and is suitable for handling imbalanced data for numerical data types. The SMOTE-RkNN method integrates SMOTE and rough-set techniques to oversample and control new instances, respectively [19]. SMOTE-RkNN addresses imbalanced data by identifying noise based on probability density instead of noisy neighborhoods when creating new samples.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority oversampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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“…Instead of relying on a single decision tree, the random forest takes predictions from each tree and predicts the final output based on the majority votes of the predictions. A larger number of trees in the forest provides higher accuracy and avoids the problem of overfitting (Breiman, 2001; Chumachenko et al., 2022; Zhang et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

Prediction of drinking water quality with machine learning models: A public health nursing approach

Özsezer,

Mermer

2023

Public Health Nursing

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ObjectiveThe aim of this study is to use machine learning models to predict drinking water quality from a public health nursing approach.DesignMachine learning study.Sample“Water Quality Dataset” was used in the study. The dataset contains physical and chemical measurements of water quality for 2400 different water bodies. The process consists of four stages: Data processing with Synthetic Minority Oversampling Technique, hyperparameter tuning with 10‐fold cross‐validation, modeling and comparative analysis. 80% of the dataset is allocated as training data and 20% as test data. ML models logistic regression, K‐nearest neighbor, support vector machine, random forest, XGBoost, AdaBoost Classifier, Decision Tree algorithms were used for water quality prediction. Accuracy, precision, recall, F1 score and AUC performance metrics of ML models were compared. To evaluate the performance of the models, 10‐fold cross‐validation was used and a comparative analysis was performed. The p‐values of the models were also compared.ResultsN this study, where drinking water quality was predicted with seven different ML algorithms, it can be said that XGBoost and Random Forest are the best classification models in all performance metrics. There is a significant difference in all ML algorithms according to the p‐value. The H0 hypothesis is accepted for these algorithms. According to the H0 hypothesis, there is no difference between actual values and predicted values.ConclusionIn conclusion, the use of ML models in the prediction of drinking water quality can help nurses greatly improve access to clean water, a human right, be more knowledgeable about water quality, and protect the health of individuals.

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“…RB‐CCR uses the class potential concept to improve the energy‐based resampling method of CCR. Zhang et al 45 proposed a more robust and general hybrid mutation algorithm, SMOTE reverse k‐nearest neighbor algorithm (SMOTE‐RkNN). This algorithm identifies noise based on probability density rather than local neighborhood information.…”

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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SMOTE-RkNN: A hybrid re-sampling method based on SMOTE and reverse k-nearest neighbors

Cited by 65 publications

References 45 publications

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

Prediction of drinking water quality with machine learning models: A public health nursing approach

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

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