The utiml package is a framework for the application of classification algorithms to multi-label data. Like the well known MULAN used with Weka, it provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. The package was designed to allow users to easily perform complete multi-label classification experiments in the R environment.
Currently, the main methods supported are:
Binary Relevance (BR),
Calibrated Label Ranking (CLR),
ConTRolled Label correlation exploitation (CTRL),
Dependent Binary Relevance (DBR),
Ensemble of Binary Relevance (EBR),
Ensemble of Classifier Chains (ECC),
Ensemble of Pruned Set (EPS),
Hierarchy Of Multilabel classifiER (HOMER),
Label specIfic FeaTures (LIFT),
Label Powerset (LP),
Meta-Binary Relevance (MBR or 2BR),
Multi-label KNN (ML-KNN),
Nested Stacking (NS),
Pruned Problem Transformation (PPT),
Pruned and Confident Stacking Approach (Prudent),
Pruned Set (PS),
Random k-labelsets (RAkEL),
Recursive Dependent Binary Relevance (RDBR),
Ranking by Pairwise Comparison (RPC)
Performing a cross-validation procedure,
Fill sparse 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 protected]>
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
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