Synthetic Minority Oversampling Technique

The Synthetic Minority Oversampling Technique (SMOTE) addresses the challenge of class imbalance in machine learning datasets, where one class significantly outnumbers others, hindering accurate model training. Current research focuses on improving SMOTE's effectiveness through variations like Minimum Enclosing Ball SMOTE (MEB-SMOTE) and integrating it with other techniques such as deep learning models (e.g., convolutional neural networks, generative adversarial networks) and data augmentation methods (e.g., Mixup). This work is crucial for enhancing the performance of machine learning models across diverse applications, including medical diagnosis, fraud detection, and cybersecurity, where imbalanced datasets are common. Recent studies also explore the theoretical underpinnings of SMOTE and its variants, aiming to better understand its strengths and limitations.

Papers

September 29, 2024

Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification
Mohamed Abdelhamid, Abhyuday Desai
Class Imbalance Binary Classification Comprehensive Study Ghanaian Musical Scale Synthetic Minority Oversampling Technique Class Weight

September 27, 2024

Early diagnosis of Alzheimer's disease from MRI images with deep learning model
Sajjad Aghasi Javid, Mahmood Mohassel Feghhi
Convolutional Neural Network Deep Learning Model Alzheimer'S Disease Magnetic Resonance Imaging Common Disease Magnetic Resonance Image Pre Trained Convolutional Neural Network Early Diagnosis Synthetic Minority Oversampling Technique

August 7, 2024

Minimum Enclosing Ball Synthetic Minority Oversampling Technique from a Geometric Perspective
Yi-Yang Shangguan, Shi-Shun Chen, Xiao-Yang Li
Class Imbalance Synthetic Minority Oversampling Technique Minority Sample

June 5, 2024

Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
Ryumei Nakada, Yichen Xu, Lexin Li, Linjun Zhang
Large Language Model Synthetic Data Theoretical Understanding Imbalanced Data Data Imbalance Oversampling Method Practical Approach Synthetic Minority Oversampling Technique

February 27, 2024

February 6, 2024

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Empirical Study Natural Language Oriented Variant Synthetic Minority Oversampling Technique Dynamic Rebalancing

January 25, 2024

Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation
Yumnah Hasan, Allan de Lima, Fatemeh Amerehi, Darian Reyes Fernandez de Bulnes, Patrick Healy, Conor Ryan
Machine Learning Data Augmentation Inherent Interpretability Language Evolution Breast Cancer Diagnosis Synthetic Minority Oversampling Technique Model Understanding

January 6, 2024

Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling
Ali Alfatemi, Mohamed Rahouti, Ruhul Amin, Sarah ALJamal, Kaiqi Xiong, Yufeng Xin
Synergistic Information Synthetic Minority Oversampling Technique Distributed Denial of Service

November 13, 2023

mlscorecheck: Testing the consistency of reported performance scores and experiments in machine learning
György Kovács, Attila Fazekas
Machine Learning Strong Consistency Optical Experiment Performance Score Reproducibility Crisis Synthetic Minority Oversampling Technique Machine Learning Problem Consistency Test

August 29, 2023

From SMOTE to Mixup for Deep Imbalanced Classification
Wei-Chao Cheng, Tan-Ha Mai, Hsuan-Tien Lin
Imbalanced Data Mixup Method Spatio Temporal Mixup Mechanism Imbalanced Learning Synthetic Minority Oversampling Technique Deep Imbalanced

August 28, 2023

SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams
Chun Wai Chiu, Leandro L. Minku
Class Imbalance Concept Drift Oversampling Method Synthetic Minority Oversampling Technique Minority Sample

August 5, 2023

Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory
Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
LeArning Abstract Theoretical Understanding Imbalanced Datasets Kernel Density Oversampling Method Synthetic Minority Oversampling Technique Parametric Bootstrap Imbalanced Regression

May 16, 2023

BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions
Md Manjurul Ahsan, Shivakumar Raman, Zahed Siddique
Data Imbalance Imbalanced Datasets Unbiased Learning Oversampling Method Synthetic Minority Oversampling Technique

December 19, 2022

Anticancer Peptides Classification using Kernel Sparse Representation Classifier
Ehtisham Fazal, Muhammad Sohail Ibrahim, Seongyong Park, Imran Naseem, Abdul Wahab
Sparse Representation Amino Acid Synthetic Minority Oversampling Technique Sparse Kernel Anticancer Peptide

November 18, 2022

How to train your draGAN: A task oriented solution to imbalanced classification
Leon O. Guertler, Andri Ashfahani, Anh Tuan Luu
Classification Code GAN Model Solution Path Related Task Data Generation Imbalanced Datasets Synthetic Minority Oversampling Technique

November 14, 2022

Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset
Yuru Jing
Machine Learning Imbalanced Datasets Stroke Detection Synthetic Minority Oversampling Technique Imbalanced Medical Algorithm Namely Kmeans

October 23, 2022

Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO
Md Manjurul Ahsan, Md Shahin Ali, Zahed Siddique
Generative Adversarial Network Class Imbalance Imbalanced Classification Oversampling Method Synthetic Minority Oversampling Technique

September 1, 2022

Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks
Javad Hassannataj Joloudari, Abdolreza Marefat, Mohammad Ali Nematollahi, Solomon Sunday Oyelere, Sadiq Hussain
Convolutional Neural Network Imbalanced Data Persian Dataset Feature Normalization Synthetic Minority Oversampling Technique Imbalance Learning

January 14, 2022

Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique
Andrew Baumgartner, Sevda Molani, Qi Wei, Jennifer Hadlock
Time Series Classification Imputation Method Time Series Imputation Partial Observation Irregular Time Series Synthetic Minority Oversampling Technique Imbalance Learning

Synthetic Minority Oversampling Technique

Papers

Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification

Early diagnosis of Alzheimer's disease from MRI images with deep learning model

Minimum Enclosing Ball Synthetic Minority Oversampling Technique from a Geometric Perspective

Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance

A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)

Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants

Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation

Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling

mlscorecheck: Testing the consistency of reported performance scores and experiments in machine learning

From SMOTE to Mixup for Deep Imbalanced Classification

SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams

Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions

Anticancer Peptides Classification using Kernel Sparse Representation Classifier

How to train your draGAN: A task oriented solution to imbalanced classification

Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset

Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO

Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks

Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique