🔥🔥High-Performance Face Recognition Library on PyTorch🔥🔥

A curated list of awesome imbalanced learning papers, codes, frameworks, and libraries. | 类别不平衡学习：论文、代码、框架与库

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

ICDE'20 | A general & effective ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

《机器学习之类别不平衡问题》文章代码

Final career project on PAC theory and imbalanced datasets

Theano implementation of Cost-Sensitive Deep Neural Networks

Implementation of the Geometric SMOTE over-sampling algorithm.

P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.

The objective of this work is to develop machine learning (ML) methods that can accurately predict adverse drug reactions (ADRs) using the databases SIDER and OFFSIDES.

SMOGN: a Pre-processing Approach for Imbalanced Regression - LIDTA2017

Learn and Explore

SOUL: Scala Oversampling and Undersampling Library.

Adversarial labeller is a sklearn compatible instance labelling tool for model selection under data drift.

Develop a knowledge-based approach using MSK-IMPACT data to build an automatic variant classifier

Examples of algorithms dealing with imbalanced data.

Text Classification with Scikit-Learn, Imbalanced-Learn and the NLTK

ESmote - An R package implemneting fast SMOTE algorithm

Papers on imbalanced learning

Machine Learning Project on Imbalanced Data in R

Group Stratified Shuffle Split cross validator for data science projects

An R Package with Boosting and SMOTEBoost implementations for Regression Tasks

A new metric to measure multi-class imbalance degree of data using likelihood ratio test based on the paper LRID by Rui Zhu et. al.

Master Thesis

Text classification with scikit-learn, used to make predictions for Kaggle Spooky Author Identification competition

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

Loan Delinquency Prediction from Analytics Vidya

This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

Imbalanced learning add-ons for PyTorch

Kaggle Challenge