utiml: Utilities for Multi-Label Learning


The utiml package is a framework to support multi-label processing, like Mulan on Weka. The main utiml advantage is because it is in R, that in other others, it is simple to use and extend.


Currently, the main methods supported are:

  1. Classification methods: Binary Relevance (BR), BR+, Classifier Chains, ConTRolled Label correlation exploitation (CTRL), Dependent Binary Relevance (DBR), Ensemble of Binary Relevance (EBR), Ensemble of Classifier Chains (ECC), Meta-Binary Relevance (MBR or 2BR), Nested Stacking (NS), Pruned and Confident Stacking Approach (Prudent), Recursive Dependent Binary Relevance (RDBR)

  2. Evaluation methods: Confusion Matrix, Evaluate, Supported measures

  3. Pre-process utilities: Fill sparce data, Normalize data, Remove attributes, Remove labels, Remove skewness labels, Remove unique attributes, Remove unlabeled instances, Replace nominal attributes

  4. Sampling methods: Create holdout partitions, Create k-fold partitions, Create random subset, Create subset, Partition fold

  5. Threshold methods: Fixed threshold, MCUT, PCUT, RCUT, SCUT, Subset correction

However, there are other utilities methods not previously cited as as.bipartition, as.mlresult, as.ranking, multilabel_prediction, etc. More details and examples are available on utiml repository.


We use the mldr package, to manipulate multi-label data. See its documentation to more information about handle multi-label dataset.


  • Adriano Rivolli <rivolli@utfpr.edu.br>

This package is a result of my PhD at Institute of Mathematics and Computer Sciences (ICMC) at the University of Sao Paulo, Brazil.

PhD advisor: Andre C. P. L. F. de Carvalho

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.