Popular GitHub Repositories for Imbalance

XAI - An eXplainability toolbox for machine learning

[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

Federated Learning Benchmark - Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)

?️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库

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

A repository contains a collection of resources and papers on Imbalance Learning On Graphs

This is the code for Addressing Class Imbalance in Federated Learning (AAAI-2021).

Study of data imbalance and asynchronous aggregation algorithm on Federated Learning system (using PySyft)

Implementation of the paper "Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems"

Source code of NeurIPS 2022 paper “Co-Modality Graph Contrastive Learning for Imbalanced Node Classification”

Multitask-learning of a BERT backbone. Allows to easily train a BERT model with state-of-the-art method such as PCGrad, Gradient Vaccine, PALs, Scheduling, Class imbalance handling and many optimizations

This repo contains implementation of advanced ML techniques. Includes model ensembles, cost-sensitive learning and dealing with class imbalance.

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

My Masters' thesis along with all codes and datsets.

Deepfake faces detection from forged videos where used explainable AI for models' robustness as well as cost sensitive methods for mitigating dataset imbalance problem

DASFAA2023 FedGR Code Repository. Federated learning for double unbalance settings (sample quantities imbalance for different classes in client and label or class imbalance for different client cross-client)

osman: OverSampling by Deep Generative Models A pip package which oversamples class imbalance binary data by Deep Generative Models.

A credit card fraud detection kernel

High-performance limit order book engine with C++ core and Python SDK. Processes 20M+ msgs/sec with µs latency. Supports real crypto/equity data replay, spread/imbalance/impact analytics, and backtesting of VWAP, TWAP, POV, and market-making strategies with reproducible PnL and risk metrics.