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

Classification project - dealing with imbalanced dataset

[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

[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

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

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

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.

Papers about long-tailed tasks

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

Classification Ml problem. The goal of this project is to build a model that borrowers can use to help make the best financial decisions.(Customer will experience financial delincy in the next two years))

Some trick for handling imbalanced dataset

This notebook shows how the f1 metric differs accuracy on imbalanced data. The heart disease dataset from kaggle is used (https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease).

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

This was a comprehensive project completed as part of the Data Science PG Programme. This covers classification algorithms over a dataset collected on health/diagnostic variables to predict of a person has diabetes or not based on the data points. Apart from extensive EDA to understand the distribution and other aspects of the data. Pre-processi…

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

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

Local Feature Weight kNN combined Local kNN and Feature weighted kNN.

Identify and classify toxic commentary

Develop a neural network model which classify cars, trucks and cats, while dealing with imbalanced dataset. In addition, generate an adversarial image that designed to deceive the trained model.