imbalance-classification

ICDE'20 | A general & effective 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).

ResLT: Residual Learning for Long-tailed Recognition

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

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

The Mulan Framework with Multi-Label Resampling Algorithms

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.

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 related credit card transactions over the 2010 calendar year. The data presented a supervised problem as it included a column showing the transaction’s fraud label (whether a transaction was fraudulent or not). It also contained identifying information about each transaction such as the credit card number, merchant, merchant state, etc. The dataset had 96,753 records and 10 data fields. We first described and visualized each of the 10 data fields, cleaned the dataset, and filled in missing values. Then we created many variables and performed feature selection. Finally, we created a variety of machine learning models (both linear and nonlinear) and highlighted our results.

Identify and classify toxic commentary

Deal with imbalanced classification problem in credit card fraud detection

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

Trying to solve a imbalanced little data in text sentiment analysis

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

In class Kaggle competition on predicting bankruptcy of a firm

Papers about long-tailed tasks

Predicting the status (acquired, open or closed) of a company using Crunchbase data

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

Unbalanced data classification

This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.

Using machine learning methods to predict COVID-19 diagnoses in the Swiss population.

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

Classification project - dealing with imbalanced dataset

This project is about detecting fraudulent credit card transactions. The dataset tends to be highly imbalanced, with less than 0.2% of the observations labelled as fraudulent. To address this issue we have to take into account the bank's objective (maximizing precision or recall) and restrictions. The performance and efficiency of many classification algorithms (Logistic Regression, XGBoost, Random Forests) were tested and compared.

Déploiement d'une API Flask du modèle de classification déployée sur Heroku (OpenClassrooms | Data Scientist | Projet 7)

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.

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

Algorithms used to confirm whether a celestial body is a planet or not.