ECLARE: Extreme Classification with Label Graph Correlations

Label-Representative Graph Convolutional Network for Multi-Label Text Classification

Repository of the paper "Community Detection Methods for Multi-Label Classification" publish in BRACIS 2023

This repository contains code for visualizing hybrid partitions, a method that plays a significant role in my PhD thesis. The code is designed to plot data partitions, specifically to highlight the differences between label clusters and instance clusters.

This code is a part of my doctoral research at PPG-CC/DC/UFSCar. HPML-J is the name of the first experiment carried out: Hybrid Partitions for Multi-Label Classification with index Jaccard.

This repository hold all experiments conducted during my PhD (2019-2023). HPML means "Hybrid Partitions for Multi-Label Classification". SET-UP-1

The code of SGCN for FMLC

PyTorch implementation of 'How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents'

Generates hybrid partitions using community detection methods.

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 a part of my doctoral research at PPG-CC/DC/UFSCar in colaboration with Ku Leuven in Belgium.