There are 9 repositories under imbalanced-classification topic.
😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切:论文、代码、框架与库
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库
Code repository for the online course Machine Learning with Imbalanced Data
[NeurIPS 2020] Balanced Meta-Softmax for Long-Tailed Visual Recognition
[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
This is the official PyTorch implementation of the paper "Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning" (Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, Alan Yuille).
Implementation code of RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data accepted by Medical Image Analysis Journal (MedIA 2022)
This is a multiclass classification project to classify severity of road accidents into three categories. this project is based on real-world data and dataset is also highly imbalanced.
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery, TCSVT, 2022
Code for ECML-PKDD 2022 paper "GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction"
A collection of Open Source Contributions in Learning from Imbalanced and Overlapped Data
Learning Imbalanced Datasets With Maximum Margin Losss
Flight delays prediction and analysis: Machine Learning Approach
Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). :sunflower:
DuBE: Duple-balanced Ensemble Learning from Skewed Data
Winning a competition on imbalanced image classification.
Highly multi-class machine learning classification problem in Python
Pytorch implementation of Class Balanced Loss based on Effective number of Samples
The souce code of MICCAI'23 paper: Combat Long-tails in Medical Classification with Relation-aware Consistency and Virtual Features Compensation
RuleCOSI is a machine learning package that combine and simplifies tree ensembles and generates a single rule-based classifier that is smaller and simpler.
Deployment of a classification model on a webapp using FLASK for the backend and html/CSS/JS for frontend
Backorders are unavoidable, but by anticipating which things will be backordered, planning can be streamlined at several levels, preventing unexpected strain on production, logistics, and transportation. ERP systems generate a lot of data (mainly structured) and also contain a lot of historical data; if this data can be properly utilized, a predictive model to forecast backorders and plan accordingly can be constructed. Based on past data from inventories, supply chain, and sales, classify the products as going into backorder or not.
RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem
Multilabel classification to categorise messages received during a disaster
Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
A very interesting repo towards Alzheimer disease (Healthcare) contains 2 important Notebooks one with handling the imbalance data and other without significantly handling the imbalance.
Build a fastText product classification model that can predict a normalized category name for a product, given an unstructured textual representation.
Implement machine learning models which are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.