Popular GitHub Repositories for Imbalanced Data

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

Synthetic Minority Over-Sampling Technique for Regression

An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems

Classification and Gradient-based Localization of Chest Radiographs using PyTorch.

Code for the paper: Multi-Label Clinical Time-Series Generation via Conditional GAN (IEEE TKDE)

[Paper][LREC-COLING 2024] Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion

Implementation code of RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data accepted by Medical Image Analysis Journal (MedIA 2022)

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

This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

This repository contains the image classification followed by semantic segmentation of Chest X-Rays to detect a clinical condition called Pneumothorax.

Deep Learning on Weld Image Generation using DCGAN and BAGAN-GP

This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.

My quick take on matching algorithms using RF, XGBoost on an imbalanced dataset.

A machine learning web app which predicts whether an employee gets promoted or not

Implementation of a comparative analysis to find the best ML model for classifying dry eye disease from healthy controls using metabolomics datasets.

Applying Transformer-based models to the imbalanced multi-label Reuters News Dataset text classification task.

Contained in this repository are the Jupyter notebooks that contain the scripts used in this project. Examples include: exploratory data analysis, creation of training, validation and test data sets, and CNN model development and data extraction.

The objective of my experiment is to analyze the performance of Random Forest, Naive Bayes, Logistic Regression, and K-nearest neighbors machine learning models for evaluation of the utility of synthesized data for fraud detection.

In this project, we build and train a model to predict if a customer will defer on a particular loan on an imbalanced dataset. We'll build a layered ANN for this and try to make our model better using Hyperparamater Optimization, before exploring Oversampling to make it more accurate.

Kernels for machine learning problems