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:
Binary Relevance (BR),
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)
Fill sparce data,
Remove skewness labels,
Remove unique attributes,
Remove unlabeled instances,
Replace nominal attributes
Create holdout partitions,
Create k-fold partitions,
Create random subset,
However, there are other utilities methods not previously cited as
multilabel_prediction, etc. More
details and examples are available on
We use the
mldr package, to manipulate multi-label data.
See its documentation to more information about handle multi-label dataset.
Adriano Rivolli <email@example.com>
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