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Topic: "imbalance-classification"

ZhiningLiu1998/self-paced-ensemble

[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

Language: Python - Size: 1.44 MB - Last synced at: 19 days ago - Pushed at: about 1 year ago - Stars: 257 - Forks: 50

ZhiningLiu1998/mesa

[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

Language: Jupyter Notebook - Size: 1.62 MB - Last synced at: 12 days ago - Pushed at: 10 months ago - Stars: 106 - Forks: 26

balanced-fl/Addressing-Class-Imbalance-FL

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

Language: Python - Size: 44.9 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 66 - Forks: 14

YijinHuang/pytorch-classification

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

Language: Python - Size: 109 KB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 62 - Forks: 20

jiequancui/ResLT

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

Language: Python - Size: 12.9 MB - Last synced at: 5 months ago - Pushed at: over 1 year ago - Stars: 57 - Forks: 5

GZWQ/Awesome-Long-Tailed

Papers about long-tailed tasks

Size: 23.4 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 48 - Forks: 5

farhantandia/Tricks-for-Handling-Imbalanced-Dataset-Image-Classification

Some trick for handling imbalanced dataset

Language: Jupyter Notebook - Size: 228 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 18 - Forks: 6

lvyilin/DGC

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

Language: Python - Size: 1020 KB - Last synced at: about 1 year ago - Pushed at: about 4 years ago - Stars: 14 - Forks: 4

mrinal1704/Credit-Card-Transaction-Fraud-Detection-using-Supervised-Machine-learning-with-an-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 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.

Language: Jupyter Notebook - Size: 10.8 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 7 - Forks: 5

HaoWeiHe/Multi-label-classification

Identify and classify toxic commentary

Language: Python - Size: 3.19 MB - Last synced at: 7 months ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 1

baguspurnama98/BM-FKNCN

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

Language: Jupyter Notebook - Size: 1.71 MB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 1

spapicchio/Gamma-Telescope-Analysis

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

Language: Jupyter Notebook - Size: 48.9 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 0

iamdanialkamali/ImbalancedLittleData

Trying to solve a imbalanced little data in text sentiment analysis

Language: Jupyter Notebook - Size: 27.3 KB - Last synced at: about 1 month ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 1

m0h1t98/Credit_Card_Fraud

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.

Language: Jupyter Notebook - Size: 449 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 1

saminens/Predicting-Bankruptcy-of-firms

In class Kaggle competition on predicting bankruptcy of a firm

Language: Jupyter Notebook - Size: 414 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 2 - Forks: 0

rodolfomp123/imb-mulan

The Mulan Framework with Multi-Label Resampling Algorithms

Language: HTML - Size: 19 MB - Last synced at: almost 2 years ago - Pushed at: about 5 years ago - Stars: 2 - Forks: 3

chenzhivis/Analysis-and-Classification-of-Restaurant-Reviews

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

Language: Jupyter Notebook - Size: 4.15 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 2

mmkamani7/Targeted-Meta-Learning

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

Language: Python - Size: 217 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 1

SmellyArmure/OC_DS_Project7_api_flask

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

Language: Python - Size: 15.5 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0

CRDK1009/Exploring-Planets

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

Language: Jupyter Notebook - Size: 188 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

namnguyen0690/Credit_Card_Fraud_Detection

Deal with imbalanced classification problem in credit card fraud detection

Language: Jupyter Notebook - Size: 1000 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

pranaysingh25/IBM-Employee-Attrition-Analysis

Built a model using XGBoost that predicts the chances of Attrition of an employee working at IBM with 84% Precision.

Language: Jupyter Notebook - Size: 1.08 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 0

safwanshamsir99/HLA-Data-Science-Assessment

Data Science Assessment from HLA

Language: Jupyter Notebook - Size: 1.62 MB - Last synced at: 22 days ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

thefifthagreement/jedha-fs-s3-project

Classification project - dealing with imbalanced dataset

Language: Jupyter Notebook - Size: 11.2 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

Owerre/anomaly-detection

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

Language: Jupyter Notebook - Size: 9.25 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 2

MLOPTPSU/Targeted-Meta-Learning

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

Language: Python - Size: 207 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 1

WithDD97/Loan-Approval-

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))

Size: 20.6 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

marizombie/f1-score-vs-accuracy

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).

Language: Jupyter Notebook - Size: 22.5 KB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0

girish004/Data-Science-Playground

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

Language: Jupyter Notebook - Size: 4.49 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 1

Kiminjo/LFWkNN

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

Language: Jupyter Notebook - Size: 873 KB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

OdedMous/Imbalanced-Dataset

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.

Language: Python - Size: 2.74 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

josecruzado21/credit_card_fraud_detection

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.

Language: Jupyter Notebook - Size: 170 KB - Last synced at: about 1 year ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

nandinib1999/credit-card-fraud-detection

Using the Kaggle dataset of credit card fraud detection, I have applied the techniques of both undersampling (with Autoencoders) and oversampling (SMOTE) to predict the credit card default.

Language: Jupyter Notebook - Size: 659 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

vmieres/Machine-Learning

This repo is about Machine Learning and Classification

Language: Jupyter Notebook - Size: 48.8 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 1

cknotz/covid_mlearn_python

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

Language: Python - Size: 115 KB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

jiko23/jayanta

Unbalanced data classification

Language: Python - Size: 4.05 MB - Last synced at: about 2 months ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0

xaaronx/crunchbase-challenge

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

Language: Jupyter Notebook - Size: 3.14 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 0

pranaysingh25/Credit-Card-Fraud-Detection

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

Language: Jupyter Notebook - Size: 295 KB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1

Related Topics
imbalanced-data 14 imbalanced-learning 10 machine-learning 8 xgboost 5 classification 4 class-imbalance 4 python 4 logistic-regression 3 tensorflow2 3 smote 3 long-tail 2 bert 2 fraud-detection 2 ensemble 2 training 2 long-tailed-recognition 2 tensorflow 2 ml 2 meta-learning 2 deep-learning 2 bilevel-optimization 2 bias 2 ensemble-model 2 imbalanced-learn 2 python3 2 feature-engineering 2 ensemble-machine-learning 2 classification-algorithm 2 random-forest 2 random-forest-classifier 2 credit-risk 2 supervised-learning 2 sklearn 2 gridsearchcv 2 deep-neural-networks 2 xgboost-algorithm 2 svm-classifier 2 stacked-ensembles 2 accuracy 1 stacking-ensemble 1 randomsearch-cv 1 informed-search 1 extreme-gradient-boosting 1 resampling-methods 1 creditcard-fraud 1 seaborn 1 ibm-attrition 1 attrition 1 regression-analysis 1 credit-card-fraud 1 knn-classification 1 knn 1 fuzzy-logic 1 fuzzy-classification 1 bonferroni 1 deep-generative-model 1 undersampling 1 smoteenn 1 sklearn-library 1 sklearn-classify 1 resampling 1 oversampling 1 naive-random-oversampler 1 pypi 1 meta-sampler 1 meta-learning-algorithms 1 mesa 1 meta-training 1 ensemble-learning 1 ensemble-methods 1 emsembling 1 supervised-classification-methods 1 supervised-machine-learning 1 feature-extraction 1 visualization 1 transfer-learning 1 toxic-comment-classification 1 multilabel-classification 1 lstm-neural-networks 1 keras-tensorflow 1 huggingface 1 recall 1 precision 1 neural-network 1 keras-neural-network 1 covid19 1 scikitlearn-machine-learning 1 nlp-classification-model 1 natural-language-processing 1 f1score 1 f1-score 1 comparison 1 accuracy-metrics 1 evaluating-models 1 k-nearest-neighbors 1 scikit-learn 1 autoencoder 1 neural-network-training 1 loss-functions 1 federated-learning 1