Oversampling Method

Oversampling methods aim to address class imbalance in datasets by artificially increasing the number of instances in underrepresented classes, improving the performance of machine learning models. Current research focuses on developing sophisticated oversampling techniques using diverse approaches, including generative adversarial networks (GANs), autoencoders, diffusion models, and large language models (LLMs), often combined with other strategies like undersampling or filtering. These advancements are crucial for various applications, from improving the accuracy of medical diagnoses and fraud detection to enhancing the robustness of network intrusion detection systems and other machine learning tasks hampered by skewed data distributions. The ongoing development of more efficient and effective oversampling methods is vital for advancing the reliability and fairness of machine learning across numerous domains.

Papers

November 1, 2024

A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
Ismail Hakki Karaman, Gulser Koksal, Levent Eriskin, Salih Salihoglu
Machine Learning Raw Data Multi Label Classification Multi Label Text Classification Oversampling Method Class Imbalanced Data Unlabeled Dataset

September 24, 2024

Interpolation filter design for sample rate independent audio effect RNNs
Alistair Carson, Alec Wright, Stefan Bilbao
Recurrent Neural Network Fractional Derivative Oversampling Method Sampling Rate Filter Design

September 9, 2024

HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
Ziming Zhao, Tiehua Zhang, Zijian Yi, Zhishu Shen
Multi Modal Nested Hypergraphs Hypergraph Learning Oversampling Method Imbalanced Node Classification Hypergraph Model

August 23, 2024

AEMLO: AutoEncoder-Guided Multi-Label Oversampling
Ao Zhou, Bin Liu, Jin Wang, Kaiwei Sun, Kelin Liu
Multi Label Oversampling Method Label Scenario

July 20, 2024

Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise
Haoran Shen, Chen Zeng, Jiahui Wang, Qiao Wang
Industrial Disturbing Noise Kolmogorov Arnold Network Simple Function Oversampling Method Surprising Effectiveness Learned Kernel

June 10, 2024

Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing
Alistair Carson, Alec Wright, Jatin Chowdhury, Vesa Välimäki, Stefan Bilbao
Recurrent Neural Network Audio Effect Oversampling Method RNN Cell Structure

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

May 15, 2024

Restoring balance: principled under/oversampling of data for optimal classification
Emanuele Loffredo, Mauro Pastore, Simona Cocco, Rémi Monasson
Raw Data Class Imbalance Generalization Error Balancing Strategy Linear Classifier Optimal Classifier Oversampling Method

March 9, 2024

SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data
Ming Zheng, Yang Yang, Zhi-Hang Zhao, Shan-Chao Gan, Yang Chen, Si-Kai Ni, Yang Lu
Imbalanced Data Imbalanced Datasets Oversampling Method Residual Diffusion

February 13, 2024

Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data
Lingyun Zhong
Classification Code Class Imbalance Fraud Detection Imbalanced Data Oversampling Method Financial Fraud Detection SMOTE Integrated

February 3, 2024

Diabetes detection using deep learning techniques with oversampling and feature augmentation
María Teresa García-Ordás, Carmen Benavides, José Alberto Benítez-Andrades, Héctor Alaiz-Moretón, Isaías García-Rodríguez
Deep Learning Convolutional Neural Network Barzilai Borwein Technique Feature Augmentation Type 2 Diabetes Oversampling Method

January 22, 2024

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Khondokar Fida Hasan, Selina Sharmin, Salem A. Alyami, Mohammad Ali Moni
New Machine Network Intrusion Detection Intrusion Detection System Imbalanced Data Content Based Feature Oversampling Method Network Intrusion Network Intrusion Detection System Cse Cic Ids2018

October 9, 2023

Efficient Hybrid Oversampling and Intelligent Undersampling for Imbalanced Big Data Classification
Carla Vairetti, José Luis Assadi, Sebastián Maldonado
Imbalanced Classification Large Datasets Oversampling Method Prediction Bias SMOTE Integrated Resampling Strategy

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

July 7, 2023

DWReCO at CheckThat! 2023: Enhancing Subjectivity Detection through Style-based Data Sampling
Ipek Baris Schlicht, Lynn Khellaf, Defne Altiok
Oversampling Method Subjectivity Detection

May 23, 2023

Fair Oversampling Technique using Heterogeneous Clusters
Ryosuke Sonoda
Class Imbalance Oversampling Method Heterogeneous Cluster

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

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 22, 2022

Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines
Anmol Bansal, Arjun Choudhry, Anubhav Sharma, Seba Susan
Sentiment Analysis Social Medium Covid 19 Domain Specific Oversampling Method COVID 19 Vaccine

Oversampling Method

Papers

A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data

Interpolation filter design for sample rate independent audio effect RNNs

HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications

AEMLO: AutoEncoder-Guided Multi-Label Oversampling

Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise

Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing

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

Restoring balance: principled under/oversampling of data for optimal classification

SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data

Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data

Diabetes detection using deep learning techniques with oversampling and feature augmentation

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Efficient Hybrid Oversampling and Intelligent Undersampling for Imbalanced Big Data Classification

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

Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

DWReCO at CheckThat! 2023: Enhancing Subjectivity Detection through Style-based Data Sampling

Fair Oversampling Technique using Heterogeneous Clusters

BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions

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

Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines