A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Synthetic Minority Over-Sampling Technique for Regression

Synthetic Minority Over-sampling Technique

Dealing with class imbalance problem in machine learning. Synthetic oversampling(SMOTE, ADASYN).

A repository of resources for understanding the concepts of machine learning/deep learning. 

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

Address imbalance classes in machine learning projects.

Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets

Data mining with Go.

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

ICSE'18: Tuning Smote

Apply 7 common Machine Learning Algorithms to detect fraud, while dealing with imbalanced dataset

The machine learning project on UCI imbalanced data.

Learn and Explore

Implementation of SMOTE - Synthetic Minority Over-sampling Technique in SparkML / MLLib

Machine Learning Telecom Churn Model

Which one of five German authors can text be attributed to?

Comparison of FFT, DE_RF against SMOTUNED

ESmote - An R package implemneting fast SMOTE algorithm

CART and C4.5 decision trees, Synthetic Minority Over-sampling Techniques, and visualizations in R.

This repository is for MATLAB code for balancing of multiclass data by SMOTE

SMOTE-MR: A distributed Synthetic Minority Oversampling Technique (SMOTE) for Big Data which applies a MapReduce based-approach. SMOTE-MR is categorized as an `approximated/ non exact` solution. Also, there is an `exact` solution called SMOTE-BD written by the author (See: https://github.com/majobasgall/smote-bd)

Synthetic Minority Over-sampling Technique Implementation

The pattern recognition assignments and solutions for fall 2019 by Dr. Analouei at Iran University of Science and Technology

Learning how to analyze imbalanced Data, implementing SMOTE and using unbalanced R package

The classification goal is to predict whether the client will subscribe (1/0) to a term deposit (variable y).

This repository presents the code for digital modulation detection in Communication networks

Using Densenet for image classification in PyTorch