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Methods/SMOTE

SMOTE

Synthetic Minority Over-sampling Technique.

Computer VisionIntroduced 2000156 papers
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Description

Perhaps the most widely used approach to synthesizing new examples is called the Synthetic Minority Oversampling Technique, or SMOTE for short. This technique was described by Nitesh Chawla, et al. in their 2002 paper named for the technique titled “SMOTE: Synthetic Minority Over-sampling Technique.”

SMOTE works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line.

Papers Using This Method

CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction2025-06-18A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI2025-06-08Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data2025-05-19Data Balancing Strategies: A Survey of Resampling and Augmentation Methods2025-05-17Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights2025-05-08VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification2025-04-30iHHO-SMOTe: A Cleansed Approach for Handling Outliers and Reducing Noise to Improve Imbalanced Data Classification2025-04-17Kernel-Based Enhanced Oversampling Method for Imbalanced Classification2025-04-12Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention2025-04-11Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals2025-04-09Machine Learning for Identifying Potential Participants in Uruguayan Social Programs2025-03-31Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring2025-03-26Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques2025-03-13ICPR 2024 Competition on Rider Intention Prediction2025-03-11Towards species' classification of the \textit{Anastrepha pseudoparallela} group2025-03-11Machine learning algorithms to predict stroke in China based on causal inference of time series analysis2025-03-10Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records2025-03-08Simplicial SMOTE: Oversampling Solution to the Imbalanced Learning Problem2025-03-05LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation2025-03-04Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification2025-02-26