scikit-learn cross validators for iterative stratification of multilabel data

CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline for pedestrian attribute recognition and multi-label classification.

Unsupervised multilabel image segmentation (color/gray/multichannel) based on the Potts model (aka piecewise constant Mumford-Shah model)

Classification of scientific papers

Multilabel image classification with softmax by python and tensorflow

Hierarchical Multi Label Hate Speech and Abusive Language Classification

A python library to agnostically explain multi-label black-box classifiers (tabular data)

The Mulan Framework with Multi-Label Resampling Algorithms

Web UI for labelling dataset images for supervised learning support multilabel.

Supplemental material for the paper "Facilitating Prediction of Adverse Drug Reactions by Using Knowledge Graphs and Multi-Label Learning Models".

A repository of my study about multilabel stratification and classification measures.

Provide static labels to your application, whichever language you want

Multi-label stratified splits, while preserving group independence. Includes a stratification chart and report.

This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.

This code is part of my doctoral research. It's oracle experimentation of Bell Partitions using the CLUS framework.

In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients.

This code is part of my doctoral research. The aim choose the best partition generated.

Predicting categories of scientific papers with advanced machine learning techniques involving class imbalance in multi-label data and explainable machine learning.