utiml: 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) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and Ventura, S. (2015) <doi:10.1145/2716262>.

Install the latest version of this package by entering the following in R:
AuthorAdriano Rivolli [aut, cre]
Date of publication2017-04-06 05:38:52 UTC
MaintainerAdriano Rivolli <rivolli@utfpr.edu.br>
LicenseGPL | file LICENSE

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Man pages

as.bipartition: Convert a mlresult to a bipartition matrix

as.matrix.mlresult: Convert a mlresult to matrix

as.mlresult: Convert a matrix prediction in a multi label prediction

as.probability: Convert a mlresult to a probability matrix

as.ranking: Convert a mlresult to a ranking matrix

baseline: Baseline reference for multilabel classification

br: Binary Relevance for multi-label Classification

brplus: BR+ or BRplus for multi-label Classification

cc: Classifier Chains for multi-label Classification

clr: Calibrated Label Ranking (CLR) for multi-label Classification

compute_multilabel_predictions: Compute the multi-label ensemble predictions based on some...

create_holdout_partition: Create a holdout partition based on the specified algorithm

create_kfold_partition: Create the k-folds partition based on the specified algorithm

create_random_subset: Create a random subset of a dataset

create_subset: Create a subset of a dataset

ctrl: CTRL model for multi-label Classification

dbr: Dependent Binary Relevance (DBR) for multi-label...

ebr: Ensemble of Binary Relevance for multi-label Classification

ecc: Ensemble of Classifier Chains for multi-label Classification

eps: Ensemble of Pruned Set for multi-label Classification

fill_sparce_mldata: Fill sparce dataset with 0 or " values

fixed_threshold: Apply a fixed threshold in the results

homer: Hierarchy Of Multilabel classifiER (HOMER)

is.bipartition: Test if a mlresult contains crisp values as default

is.probability: Test if a mlresult contains score values as default

lcard_threshold: Threshold based on cardinality

lift: LIFT for multi-label Classification

lp: Label Powerset for multi-label Classification

mbr: Meta-BR or 2BR for multi-label Classification

mcut_threshold: Maximum Cut Thresholding (MCut)

merge_mlconfmat: Join a list of multi-label confusion matrix

mldata: Fix the mldr dataset to use factors

mlpredict: Prediction transformation problems

mltrain: Build transformation models

multilabel_confusion_matrix: Compute the confusion matrix for a multi-label prediction

multilabel_evaluate: Evaluate multi-label predictions

multilabel_measures: Return the name of all measures

multilabel_prediction: Create a mlresult object

normalize_mldata: Normalize numerical attributes

ns: Nested Stacking for multi-label Classification

partition_fold: Create the multi-label dataset from folds

pcut_threshold: Proportional Thresholding (PCut)

plus-.mlconfmat: Join two multi-label confusion matrix

ppt: Pruned Problem Transformation for multi-label Classification

predict.BASELINEmodel: Predict Method for BASELINE

predict.BRmodel: Predict Method for Binary Relevance

predict.BRPmodel: Predict Method for BR+ (brplus)

predict.CCmodel: Predict Method for Classifier Chains

predict.CLRmodel: Predict Method for CLR

predict.CTRLmodel: Predict Method for CTRL

predict.DBRmodel: Predict Method for DBR

predict.EBRmodel: Predict Method for Ensemble of Binary Relevance

predict.ECCmodel: Predict Method for Ensemble of Classifier Chains

predict.EPSmodel: Predict Method for Ensemble of Pruned Set Transformation

predict.HOMERmodel: Predict Method for HOMER

predict.LIFTmodel: Predict Method for LIFT

predict.LPmodel: Predict Method for Label Powerset

predict.MBRmodel: Predict Method for Meta-BR/2BR

predict.NSmodel: Predict Method for Nested Stacking

predict.PPTmodel: Predict Method for Pruned Problem Transformation

predict.PruDentmodel: Predict Method for PruDent

predict.PSmodel: Predict Method for Pruned Set Transformation

predict.RAkELmodel: Predict Method for RAkEL

predict.RDBRmodel: Predict Method for RDBR

predict.RPCmodel: Predict Method for RPC

print.BRmodel: Print BR model

print.BRPmodel: Print BRP model

print.CCmodel: Print CC model

print.CLRmodel: Print CLR model

print.CTRLmodel: Print CTRL model

print.DBRmodel: Print DBR model

print.EBRmodel: Print EBR model

print.ECCmodel: Print ECC model

print.EPSmodel: Print EPS model

print.kFoldPartition: Print a kFoldPartition object

print.LIFTmodel: Print LIFT model

print.LPmodel: Print LP model

print.majorityModel: Print Majority model

print.MBRmodel: Print MBR model

print.mlconfmat: Print a Multi-label Confusion Matrix

print.mlresult: Print the mlresult

print.NSmodel: Print NS model

print.PPTmodel: Print PPT model

print.PruDentmodel: Print PruDent model

print.PSmodel: Print PS model

print.RAkELmodel: Print RAkEL model

print.randomModel: Print Random model

print.RDBRmodel: Print RDBR model

print.RPCmodel: Print RPC model

prudent: PruDent classifier for multi-label Classification

ps: Pruned Set for multi-label Classification

rakel: Random k-labelsets for multilabel classification

rcut_threshold: Rank Cut (RCut) threshold method

rdbr: Recursive Dependent Binary Relevance (RDBR) for multi-label...

remove_attributes: Remove attributes from the dataset

remove_labels: Remove labels from the dataset

remove_skewness_labels: Remove unusual or very common labels

remove_unique_attributes: Remove unique attributes

remove_unlabeled_instances: Remove examples without labels

replace_nominal_attributes: Replace nominal attributes Replace the nominal attributes by...

rpc: Ranking by Pairwise Comparison (RPC) for multi-label...

scut_threshold: SCut Score-based method

sub-.mlresult: Filter a Multi-Label Result

subset_correction: Subset Correction of a predicted result

summary.mltransformation: Summary method for mltransformation

toyml: Toy multi-label dataset.

utiml: utiml: Utilities for Multi-Label Learning

utiml_all_measures_names: MEASURES METHODS ————– Return the tree with the...

utiml_compute_ensemble: Compute binary predictions

utiml_ensemble_average: Average vote combination for a single-label prediction

utiml_ensemble_check_voteschema: Verify if a schema vote name is valid

utiml_ensemble_majority: Majority vote combination for single-label prediction

utiml_ensemble_maximum: Maximum vote combination for single-label prediction

utiml_ensemble_method: Define the method name related with the vote schema

utiml_ensemble_minimum: Minimum vote combination for single-label prediction

utiml_ifelse: Conditional value selection

utiml_is_equal_sets: Define if two sets are equals independently of the order of...

utiml_iterative_split: Internal Iterative Stratification

utiml_labels_correlation: Phi Correlation Coefficient

utiml_labels_IG: Calculate the Information Gain for each pair of labels

utiml_lapply: Select the suitable method lapply or mclaplly

utiml_measure_accuracy: MULTILABEL MEASURES ————- Multi-label Accuracy...

utiml_measure_average_precision: Multi-label Average Precision Measure

utiml_measure_binary_accuracy: BINARY MEASURES ————– Compute the binary accuracy

utiml_measure_binary_AUC: Compute the binary AUC

utiml_measure_binary_f1: Compute the binary F1 measure

utiml_measure_binary_precision: Compute the binary precision

utiml_measure_binary_recall: Compute the binary recall

utiml_measure_coverage: Multi-label Coverage Measure

utiml_measure_f1: Multi-label F1 Measure

utiml_measure_hamming_loss: Multi-label Hamming Loss Measure

utiml_measure_is_error: Multi-label Is Error Measure

utiml_measure_macro_accuracy: Multi-label Macro-Accuracy Measure

utiml_measure_macro_AUC: Multi-label Macro-AUC Measure

utiml_measure_macro_f1: Multi-label Macro-F1 Measure

utiml_measure_macro_precision: Multi-label Macro-Precision Measure

utiml_measure_macro_recall: Multi-label Macro-Recall Measure

utiml_measure_margin_loss: Multi-label Margin Loss Measure

utiml_measure_micro_accuracy: Multi-label Micro-Accuracy Measure

utiml_measure_micro_AUC: Multi-label Macro-AUC Measure

utiml_measure_micro_f1: Multi-label Micro-F1 Measure

utiml_measure_micro_precision: Multi-label Micro-Precision Measure

utiml_measure_micro_recall: Multi-label Micro-Recall Measure

utiml_measure_names: Return the name of measures

utiml_measure_one_error: Multi-label One Error Measure

utiml_measure_precision: Multi-label Precision Measure

utiml_measure_ranking_error: Multi-label Ranking Error Measure

utiml_measure_ranking_loss: Multi-label Hamming Loss Measure

utiml_measure_recall: Multi-label Recall Measure

utiml_measure_subset_accuracy: Multi-label Subset Accuracy Measure

utiml_newdata: Return the newdata to a data.frame or matrix

utiml_normalize: Internal normalize data function

utiml_predict_binary_ensemble: Predict binary predictions

utiml_preserve_seed: Preserve current seed

utiml_random_split: Random split of a dataset

utiml_rename: Rename the list using the names values or its own content

utiml_restore_seed: Restore the current seed

utiml_stratified_split: Labelsets Stratification Create the indexes using the...

utiml_validate_splitmethod: Return the name of split method and validate if it is valid


as.bipartition Man page
as.matrix.mlresult Man page
as.mlresult Man page
as.mlresult.default Man page
as.mlresult.mlresult Man page
as.probability Man page
as.ranking Man page
baseline Man page
br Man page
brplus Man page
cc Man page
clr Man page
compute_multilabel_predictions Man page
create_holdout_partition Man page
create_kfold_partition Man page
create_random_subset Man page
create_subset Man page
ctrl Man page
dbr Man page
ebr Man page
ecc Man page
eps Man page
fill_sparce_mldata Man page
fixed_threshold Man page
fixed_threshold.default Man page
fixed_threshold.mlresult Man page
homer Man page
is.bipartition Man page
is.probability Man page
lcard_threshold Man page
lcard_threshold.default Man page
lcard_threshold.mlresult Man page
lift Man page
lp Man page
mbr Man page
mcut_threshold Man page
mcut_threshold.default Man page
mcut_threshold.mlresult Man page
merge_mlconfmat Man page
+.mlconfmat Man page
mldata Man page
mlpredict Man page
mlpredict.baseKNN Man page
mlpredict.C5.0 Man page
mlpredict.default Man page
mlpredict.emptyModel Man page
mlpredict.J48 Man page
mlpredict.majorityModel Man page
mlpredict.naiveBayes Man page
mlpredict.randomForest Man page
mlpredict.randomModel Man page
mlpredict.rpart Man page
mlpredict.SMO Man page
mlpredict.svm Man page
mlpredict.xgb.Booster Man page
[.mlresult Man page
mltrain Man page
mltrain.baseC5.0 Man page
mltrain.baseCART Man page
mltrain.baseJ48 Man page
mltrain.baseKNN Man page
mltrain.baseMAJORITY Man page
mltrain.baseNB Man page
mltrain.baseRANDOM Man page
mltrain.baseRF Man page
mltrain.baseSMO Man page
mltrain.baseSVM Man page
mltrain.baseXGB Man page
mltrain.default Man page
multilabel_confusion_matrix Man page
multilabel_evaluate Man page
multilabel_evaluate.mlconfmat Man page
multilabel_evaluate.mldr Man page
multilabel_measures Man page
multilabel_prediction Man page
normalize_mldata Man page
ns Man page
partition_fold Man page
pcut_threshold Man page
pcut_threshold.default Man page
pcut_threshold.mlresult Man page
ppt Man page
predict.BASELINEmodel Man page
predict.BRmodel Man page
predict.BRPmodel Man page
predict.CCmodel Man page
predict.CLRmodel Man page
predict.CTRLmodel Man page
predict.DBRmodel Man page
predict.EBRmodel Man page
predict.ECCmodel Man page
predict.EPSmodel Man page
predict.HOMERmodel Man page
predict.LIFTmodel Man page
predict.LPmodel Man page
predict.MBRmodel Man page
predict.NSmodel Man page
predict.PPTmodel Man page
predict.PruDentmodel Man page
predict.PSmodel Man page
predict.RAkELmodel Man page
predict.RDBRmodel Man page
predict.RPCmodel Man page
print.BRmodel Man page
print.BRPmodel Man page
print.CCmodel Man page
print.CLRmodel Man page
print.CTRLmodel Man page
print.DBRmodel Man page
print.EBRmodel Man page
print.ECCmodel Man page
print.EPSmodel Man page
print.kFoldPartition Man page
print.LIFTmodel Man page
print.LPmodel Man page
print.majorityModel Man page
print.MBRmodel Man page
print.mlconfmat Man page
print.mlresult Man page
print.NSmodel Man page
print.PPTmodel Man page
print.PruDentmodel Man page
print.PSmodel Man page
print.RAkELmodel Man page
print.randomModel Man page
print.RDBRmodel Man page
print.RPCmodel Man page
prudent Man page
ps Man page
rakel Man page
rcut_threshold Man page
rcut_threshold.default Man page
rcut_threshold.mlresult Man page
rdbr Man page
remove_attributes Man page
remove_labels Man page
remove_skewness_labels Man page
remove_unique_attributes Man page
remove_unlabeled_instances Man page
replace_nominal_attributes Man page
rpc Man page
scut_threshold Man page
scut_threshold.default Man page
scut_threshold.mlresult Man page
subset_correction Man page
summary.mltransformation Man page
toyml Man page
utiml Man page
utiml_all_measures_names Man page
utiml_compute_ensemble Man page
utiml_ensemble_average Man page
utiml_ensemble_check_voteschema Man page
utiml_ensemble_majority Man page
utiml_ensemble_maximum Man page
utiml_ensemble_method Man page
utiml_ensemble_minimum Man page
utiml_ifelse Man page
utiml_is_equal_sets Man page
utiml_iterative_split Man page
utiml_labels_correlation Man page
utiml_labels_IG Man page
utiml_lapply Man page
utiml_measure_accuracy Man page
utiml_measure_average_precision Man page
utiml_measure_binary_accuracy Man page
utiml_measure_binary_AUC Man page
utiml_measure_binary_f1 Man page
utiml_measure_binary_precision Man page
utiml_measure_binary_recall Man page
utiml_measure_coverage Man page
utiml_measure_f1 Man page
utiml_measure_hamming_loss Man page
utiml_measure_is_error Man page
utiml_measure_macro_accuracy Man page
utiml_measure_macro_AUC Man page
utiml_measure_macro_f1 Man page
utiml_measure_macro_precision Man page
utiml_measure_macro_recall Man page
utiml_measure_margin_loss Man page
utiml_measure_micro_accuracy Man page
utiml_measure_micro_AUC Man page
utiml_measure_micro_f1 Man page
utiml_measure_micro_precision Man page
utiml_measure_micro_recall Man page
utiml_measure_names Man page
utiml_measure_one_error Man page
utiml_measure_precision Man page
utiml_measure_ranking_error Man page
utiml_measure_ranking_loss Man page
utiml_measure_recall Man page
utiml_measure_subset_accuracy Man page
utiml_newdata Man page
utiml_newdata.default Man page
utiml_newdata.mldr Man page
utiml_normalize Man page
utiml-package Man page
utiml_predict_binary_ensemble Man page
utiml_predict_ensemble Man page
utiml_preserve_seed Man page
utiml_random_split Man page
utiml_rename Man page
utiml_restore_seed Man page
utiml_stratified_split Man page
utiml_validate_splitmethod Man page


tests/testthat/test_seed.R tests/testthat/test_internal.R tests/testthat/test_threshold.R tests/testthat/test_transformation.R tests/testthat/test_base_learner.R tests/testthat/test_sampling.R tests/testthat/test_evaluation.R tests/testthat/test_preprocess.R tests/testthat/test_powersetclassifiers.R tests/testthat/test_brclassifiers.R tests/testthat/test_mlresult.R tests/testthat/test_ensemble.R tests/testthat/test_pairwiseclassifiers.R tests/testthat/test_othersclassifiers.R
R/evaluation.R R/utiml.R R/method_ebr.R R/method_br.R R/mldr.R R/method_brplus.R R/method_clr.R R/threshold.R R/method_baseline.R R/method_ecc.R R/base_learner.R R/pre_process.R R/data.R R/method_eps.R R/transformation.R R/mlresult.R R/method_ppt.R R/method_ctrl.R R/sampling.R R/method_ps.R R/internal.R R/method_mbr.R R/method_rakel.R R/method_rpc.R R/method_dbr.R R/method_lift.R R/method_homer.R R/method_prudent.R R/ensemble.R R/method_rdbr.R R/method_lp.R R/zzz.R R/method_cc.R R/method_ns.R
man/create_holdout_partition.Rd man/utiml_measure_is_error.Rd man/print.EBRmodel.Rd man/print.RPCmodel.Rd man/rpc.Rd man/print.LIFTmodel.Rd man/print.PSmodel.Rd man/predict.LPmodel.Rd man/as.mlresult.Rd man/predict.BRPmodel.Rd man/mbr.Rd man/print.kFoldPartition.Rd man/utiml_measure_macro_accuracy.Rd man/predict.NSmodel.Rd man/ecc.Rd man/mcut_threshold.Rd man/ebr.Rd man/utiml_lapply.Rd man/print.mlconfmat.Rd man/utiml_measure_precision.Rd man/as.matrix.mlresult.Rd man/utiml_measure_macro_AUC.Rd man/print.RDBRmodel.Rd man/remove_unlabeled_instances.Rd man/utiml_random_split.Rd man/predict.LIFTmodel.Rd man/utiml_measure_micro_accuracy.Rd man/utiml_ensemble_check_voteschema.Rd man/predict.PruDentmodel.Rd man/utiml_measure_micro_precision.Rd man/rakel.Rd man/print.majorityModel.Rd man/normalize_mldata.Rd man/merge_mlconfmat.Rd man/predict.DBRmodel.Rd man/dbr.Rd man/utiml_measure_micro_AUC.Rd man/utiml_stratified_split.Rd man/utiml_ensemble_maximum.Rd man/baseline.Rd man/is.probability.Rd man/utiml_measure_ranking_loss.Rd man/mltrain.Rd man/lp.Rd man/predict.RPCmodel.Rd man/brplus.Rd man/print.EPSmodel.Rd man/utiml_measure_names.Rd man/utiml_predict_binary_ensemble.Rd man/print.CCmodel.Rd man/subset_correction.Rd man/predict.MBRmodel.Rd man/utiml_measure_micro_f1.Rd man/create_kfold_partition.Rd man/create_subset.Rd man/print.MBRmodel.Rd man/ps.Rd man/remove_attributes.Rd man/utiml_measure_recall.Rd man/summary.mltransformation.Rd man/print.randomModel.Rd man/create_random_subset.Rd man/clr.Rd man/print.DBRmodel.Rd man/utiml_measure_one_error.Rd man/as.probability.Rd man/scut_threshold.Rd man/utiml_measure_macro_precision.Rd man/utiml_newdata.Rd man/utiml_measure_micro_recall.Rd man/utiml_measure_binary_accuracy.Rd man/mlpredict.Rd man/utiml_ensemble_method.Rd man/toyml.Rd man/fill_sparce_mldata.Rd man/utiml_ifelse.Rd man/ctrl.Rd man/predict.HOMERmodel.Rd man/ppt.Rd man/predict.BASELINEmodel.Rd man/replace_nominal_attributes.Rd man/mldata.Rd man/multilabel_measures.Rd man/predict.ECCmodel.Rd man/ns.Rd man/fixed_threshold.Rd man/is.bipartition.Rd man/remove_skewness_labels.Rd man/predict.CTRLmodel.Rd man/multilabel_confusion_matrix.Rd man/utiml_measure_hamming_loss.Rd man/predict.CLRmodel.Rd man/print.RAkELmodel.Rd man/print.PPTmodel.Rd man/print.mlresult.Rd man/predict.EBRmodel.Rd man/utiml_measure_binary_precision.Rd man/utiml_measure_coverage.Rd man/utiml_rename.Rd man/utiml_measure_macro_f1.Rd man/print.BRmodel.Rd man/predict.CCmodel.Rd man/predict.RDBRmodel.Rd man/utiml_measure_ranking_error.Rd man/utiml_ensemble_average.Rd man/utiml_normalize.Rd man/lift.Rd man/utiml_measure_accuracy.Rd man/utiml_compute_ensemble.Rd man/print.PruDentmodel.Rd man/utiml_iterative_split.Rd man/utiml_ensemble_majority.Rd man/multilabel_evaluate.Rd man/print.NSmodel.Rd man/utiml.Rd man/print.CLRmodel.Rd man/utiml_all_measures_names.Rd man/br.Rd man/utiml_preserve_seed.Rd man/predict.RAkELmodel.Rd man/multilabel_prediction.Rd man/utiml_measure_f1.Rd man/predict.BRmodel.Rd man/prudent.Rd man/utiml_labels_IG.Rd man/utiml_measure_binary_AUC.Rd man/plus-.mlconfmat.Rd man/utiml_measure_binary_f1.Rd man/predict.EPSmodel.Rd man/pcut_threshold.Rd man/print.LPmodel.Rd man/as.bipartition.Rd man/utiml_validate_splitmethod.Rd man/predict.PPTmodel.Rd man/partition_fold.Rd man/print.CTRLmodel.Rd man/utiml_measure_binary_recall.Rd man/as.ranking.Rd man/print.BRPmodel.Rd man/eps.Rd man/utiml_measure_average_precision.Rd man/utiml_measure_margin_loss.Rd man/rcut_threshold.Rd man/cc.Rd man/utiml_measure_macro_recall.Rd man/utiml_ensemble_minimum.Rd man/remove_unique_attributes.Rd man/remove_labels.Rd man/utiml_restore_seed.Rd man/lcard_threshold.Rd man/utiml_labels_correlation.Rd man/utiml_measure_subset_accuracy.Rd man/sub-.mlresult.Rd man/rdbr.Rd man/predict.PSmodel.Rd man/utiml_is_equal_sets.Rd man/print.ECCmodel.Rd man/compute_multilabel_predictions.Rd man/homer.Rd

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