[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

Papers about long-tailed tasks

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

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

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

Some trick for handling imbalanced dataset

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

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…

A Bonferroni Mean Based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN), BM-FKNN, FKNCN, FKNN, KNN Classifier

The Mulan Framework with Multi-Label Resampling Algorithms

Identify and classify toxic commentary

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

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

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.

In class Kaggle competition on predicting bankruptcy of a firm

Trying to solve a imbalanced little data in text sentiment analysis

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

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

AmExpert 2019 - Machine Learning Hackathon