Test the best random partition generated by hierarchical community detection methods using clus framework
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Updated
Oct 30, 2023 - R
Test the best random partition generated by hierarchical community detection methods using clus framework
A modified version of Clus System for Hierarchical Multi-Label Classification.
Test the best hybrid partition generated by hierarchical community detection methods wiht k-NN sparsification using Clus Framework
This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.
Test the best hybrid partition generated by non hierarchical comunity detection methods, and k-NN sparsification, using Clus Framework.
Test the best random partition generated by non hierarchical community detection methods using Clus framework
This code is part of my Ph.D. research. Test the best hybrid partition chosen with Micro-F1 criteria using Clus framework.
This code is part of my PhD research. This code select the best partition using the CLUS framework. We choose the partition with the best Macro-F1.
This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.
Test the best hybrid partition generated by non hierarchical comunity detection methods, and threshold sparsification, using Clus Framework.
Repository of the paper "Community Detection Methods for Multi-Label Classification" publish in BRACIS 2023
This repository hold all experiments conducted during my PhD (2019-2023). HPML means "Hybrid Partitions for Multi-Label Classification". SET-UP-1
This project shows up the algorithm k-means implemented to cluster documents from the contest PAN CLEF 2O16 where the topics of the documentes are reviews and novels.
This code executes the CLUS algorithm in an R script.
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