scikit-learn cross validators for iterative stratification of multilabel data

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.

Repo. for 2023 AICOSS Hackathon Contest

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

This code is part of my Ph.D. research. This code selects the best partition using the CLUS framework. We choose the partition with the best Micro-F1.

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

This code is part of my PhD research. This code generate hybrid partitions using Kohonen to modeling the labels correlations, and HClust to partitioning the label space.

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. The aim choose the best partition generated.

This code is part of my Ph.D. research. Test the best hybrid partitions with Clus framework.

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 PhD research. This code select the best partition using the silhouete coefficient.

Hierarchical Multi Label Hate Speech and Abusive Language Classification

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

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

Classification of scientific papers

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

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

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

Predict keywords of a scientific paper based on the abstract text / scikit-learn