utiml (0.1.6)

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Utilities for Multi-Label Learning.


Multi-label learning strategies and others procedures to support multi- label classification in R. The package provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. A complete overview of the matter can be seen in Zhang, M. and Zhou, Z. (2014) and Gibaja, E. and Ventura, S. (2015) A Tutorial on Multi-label Learning.

Maintainer: Adriano Rivolli
Author(s): Adriano Rivolli [aut, cre]

License: GPL | file LICENSE

Uses: ROCR, e1071, kknn, randomForest, rpart, FSelector, infotheo, testthat, knitr, C50, rmarkdown

Released about 1 month ago.

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