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utiml  

Utilities for Multi-Label Learning
View on CRAN: Click here


Download and install utiml package within the R console
Install from CRAN:
install.packages("utiml")

Install from Github:
library("remotes")
install_github("cran/utiml")

Install by package version:
library("remotes")
install_version("utiml", "0.1.7")



Attach the package and use:
library("utiml")
Maintained by
Adriano Rivolli
[Scholar Profile | Author Map]
First Published: 2016-04-08
Latest Update: 2021-05-31
Description:
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) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and Ventura, S. (2015) A Tutorial on Multi-label Learning.
How to cite:
Adriano Rivolli (2016). utiml: Utilities for Multi-Label Learning. R package version 0.1.7, https://cran.r-project.org/web/packages/utiml. Accessed 01 May. 2025.
Previous versions and publish date:
0.1.0 (2016-04-08 10:44), 0.1.1 (2016-11-19 21:28), 0.1.2 (2017-04-06 07:38), 0.1.3 (2017-07-31 23:57), 0.1.4 (2018-04-20 02:02), 0.1.5 (2019-03-16 06:40), 0.1.6 (2020-02-07 07:30)
Other packages that cited utiml R package
View utiml citation profile
Other R packages that utiml depends, imports, suggests or enhances
Complete documentation for utiml
Functions, R codes and Examples using the utiml R package
Some associated functions: as.bipartition . as.matrix.mlconfmat . as.matrix.mlresult . as.mlresult . as.probability . as.ranking . baseline . br . brplus . cc . clr . compute_multilabel_predictions . create_holdout_partition . create_kfold_partition . create_random_subset . create_subset . cv . dbr . ebr . ecc . eps . esl . fill_sparse_mldata . fixed_threshold . foodtruck . homer . is.bipartition . is.probability . lcard_threshold . lift . lp . mbr . mcut_threshold . merge_mlconfmat . mldata . mlknn . mlpredict . mltrain . multilabel_confusion_matrix . multilabel_evaluate . multilabel_measures . multilabel_prediction . normalize_mldata . ns . partition_fold . pcut_threshold . plus-.mlconfmat . ppt . predict.BASELINEmodel . predict.BRPmodel . predict.BRmodel . predict.CCmodel . predict.CLRmodel . predict.DBRmodel . predict.EBRmodel . predict.ECCmodel . predict.EPSmodel . predict.ESLmodel . predict.HOMERmodel . predict.LIFTmodel . predict.LPmodel . predict.MBRmodel . predict.MLKNNmodel . predict.NSmodel . predict.PPTmodel . predict.PSmodel . predict.PruDentmodel . predict.RAkELmodel . predict.RDBRmodel . predict.RPCmodel . print.BRPmodel . print.BRmodel . print.CCmodel . print.CLRmodel . print.DBRmodel . print.EBRmodel . print.ECCmodel . print.EPSmodel . print.ESLmodel . print.LIFTmodel . print.LPmodel . print.MBRmodel . print.MLKNNmodel . print.NSmodel . print.PPTmodel . print.PSmodel . print.PruDentmodel . print.RAkELmodel . print.RDBRmodel . print.RPCmodel . print.kFoldPartition . print.majorityModel . print.mlconfmat . print.mlresult . print.randomModel . prudent . ps . rakel . rcut_threshold . rdbr . remove_attributes . remove_labels . remove_skewness_labels . remove_unique_attributes . remove_unlabeled_instances . replace_nominal_attributes . rpc . scut_threshold . sub-.mlresult . subset_correction . summary.mltransformation . toyml . utiml . utiml_measure_names . 
Some associated R codes: base_learner.R . cross_validation.R . data.R . ensemble.R . evaluation.R . internal.R . method_baseline.R . method_br.R . method_brplus.R . method_cc.R . method_clr.R . method_dbr.R . method_ebr.R . method_ecc.R . method_eps.R . method_esl.R . method_homer.R . method_lift.R . method_lp.R . method_mbr.R . method_mlknn.R . method_ns.R . method_ppt.R . method_prudent.R . method_ps.R . method_rakel.R . method_rdbr.R . method_rpc.R . mldr.R . mlresult.R . pre_process.R . sampling.R . threshold.R . transformation.R . utiml.R . zzz.R .  Full utiml package functions and examples
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