multilabel

scikit-learn cross validators for iterative stratification of multilabel data

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

Multilabel image classification with softmax by python and tensorflow

Classification of scientific papers

Hierarchical Multi Label Hate Speech and Abusive Language Classification

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

The Mulan Framework with Multi-Label Resampling Algorithms

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

This code is part of my doctoral research. The aim is to build, validate and test global partitions for multilabel classification using the CLUS framework.

Provide static labels to your application, whichever language you want

This code is part of my PhD research. This code select the best partition using the silhouete coefficient.

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.

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

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 CLUS framework.

This code is part of my doctoral research. The aim is to build, validate and test local partitions for multilabel classification using CLUS framework.

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

This code is part of my doctoral research. The aim is to build, validate and test exhaustive partitions for multilabel classification using CLUS framework.

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. The aim is to generate a specific version of random partitions for multilabel classification.