Machine Learning in R
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Updated
May 6, 2024 - R
Machine Learning in R
A code to execute and save cross-validation in multilabel classification
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 code executes the CLUS algorithm in an R script.
This code generate partitions for a multilabel dataset using the Rogers-Tanimoto similarity measure. We use HCLUST with 6 linkage metrics to generate several partitions. You may build the partition with the highest coefficient. This code also provide an analysis about the partitioning.
This code shows how to compute the measures of multi-label classification hand in hand.
This code generate partitions for a multilabel dataset using the Jaccard Index similarity measure. We use HCLUST with 6 linkage metrics to generate several partitions. You may build the partition with the highest coefficient. This code also provide an analysis about the partitioning.
Test the best hybrid partition generated by hierarchical community detection methods wiht threshold sparsification using clus framework
Generates hybrid partitions using community detection methods.
This code is part of my Ph.D. research. Test the best hybrid partition chosen with Macro-F1 criteria using Clus framework.
Predictive Management using Machine Learning
This code generates partitions based on bell numbers for multilabel classification.
Test the best hybrid partition generated by non hierarchical comunity detection methods, and threshold sparsification, using Clus Framework.
This code is part of my doctoral research. The aim is to build, validate and test all possible partitions for multilabel classification using CLUS framework.
This code is part of my doctoral research. The aim is to build, validate and test all possible partitions for multilabel classification using CLUS framework.
This code is part of my doctoral research. The aim choose the best partition generated.
This code is part of my doctoral research. The aim is to generate partitions using Rogers-Tanimoto similarity measure.
Compute similarities measures (categorical data) for all labels in label space for a multilabel dataset
Test the best hybrid partition generated by non hierarchical comunity detection methods, and k-NN sparsification, using Clus Framework.
This code is part of my doctoral research. It's oracle experimentation of Bell Partitions using the CLUS framework.
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