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

[NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

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

A general, feasible, and extensible framework for classification tasks.

Papers about long-tailed tasks

Some trick for handling imbalanced dataset

Credit card fraud is a burden for organizations across the globe. Specifically, $24.26 billion were lost due to credit card fraud worldwide in 2018, according to shiftprocessing.com. In this project, our goal was to build an effective and efficient model to predict fraud. We analyzed a real-world dataset that contained a list of government relat…

ResLT: Residual Learning for Long-tailed Recognition (TPAMI 2022)

Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

Developed a NLP classification model that can classify negative reviews of restaurants, help restaurant managers save time on reviewing comments, absorbing information. Analyze the service defects, help restaurants improve business

The Mulan Framework with Multi-Label Resampling Algorithms

Anomaly detection using unsupervised, semi-supervised, and supervised machine learning methods

AmExpert 2019 - Machine Learning Hackathon

In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.

This is a classification problem to detect or classify the fraud with label 0 or 1. Class with label 1 means fraud is detected otherwise 0. The biggest challenge is to handle the imbalanced data set.

A Machine learning model that detects Fraud Credit Card Transactions over a data set of anonymized credit card transactions labeled as fraudulent or genuine.

Identify and classify toxic commentary

Introductory code snippets which deals with the basics of data science and machine learning which you can rely on anytime

In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.

This repo is about Machine Learning and Classification