utiml: Utilities for Multi-Label Learning

Multi-label learning methods and others utilities to support multi- label classification in R.

Author
Adriano Rivolli [aut, cre]
Date of publication
2016-11-19 21:28:00
Maintainer
Adriano Rivolli <rivolli@utfpr.edu.br>
License
GPL | file LICENSE
Version
0.1.1
URLs

View on CRAN

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

Files in this package

utiml
utiml/inst
utiml/inst/doc
utiml/inst/doc/utiml-overview.R
utiml/inst/doc/utiml-overview.Rmd
utiml/inst/doc/utiml-overview.pdf
utiml/tests
utiml/tests/testfiles
utiml/tests/testfiles/flags-expected.csv
utiml/tests/testfiles/flags-scores.csv
utiml/tests/testfiles/flags-measures.csv
utiml/tests/testfiles/flags-bipartition.csv
utiml/tests/testthat.R
utiml/tests/testthat
utiml/tests/testthat/test_seed.R
utiml/tests/testthat/test_internal.R
utiml/tests/testthat/test_threshold.R
utiml/tests/testthat/test_transformation.R
utiml/tests/testthat/test_base_learner.R
utiml/tests/testthat/test_sampling.R
utiml/tests/testthat/test_evaluation.R
utiml/tests/testthat/test_preprocess.R
utiml/tests/testthat/test_powersetclassifiers.R
utiml/tests/testthat/test_brclassifiers.R
utiml/tests/testthat/test_mlresult.R
utiml/tests/testthat/test_ensemble.R
utiml/tests/testthat/test_pairwiseclassifiers.R
utiml/tests/testthat/test_othersclassifiers.R
utiml/NAMESPACE
utiml/NEWS.md
utiml/data
utiml/data/toyml.rda
utiml/R
utiml/R/evaluation.R
utiml/R/utiml.R
utiml/R/method_ebr.R
utiml/R/method_br.R
utiml/R/mldr.R
utiml/R/method_brplus.R
utiml/R/method_clr.R
utiml/R/threshold.R
utiml/R/method_baseline.R
utiml/R/method_ecc.R
utiml/R/base_learner.R
utiml/R/pre_process.R
utiml/R/data.R
utiml/R/method_eps.R
utiml/R/transformation.R
utiml/R/mlresult.R
utiml/R/method_ppt.R
utiml/R/method_ctrl.R
utiml/R/sampling.R
utiml/R/method_ps.R
utiml/R/internal.R
utiml/R/method_mbr.R
utiml/R/method_rakel.R
utiml/R/method_rpc.R
utiml/R/method_dbr.R
utiml/R/method_lift.R
utiml/R/method_homer.R
utiml/R/method_prudent.R
utiml/R/ensemble.R
utiml/R/method_rdbr.R
utiml/R/method_lp.R
utiml/R/zzz.R
utiml/R/method_cc.R
utiml/R/method_ns.R
utiml/vignettes
utiml/vignettes/utiml-overview.html
utiml/vignettes/utiml-overview.Rmd
utiml/README.md
utiml/MD5
utiml/build
utiml/build/vignette.rds
utiml/DESCRIPTION
utiml/man
utiml/man/create_holdout_partition.Rd
utiml/man/utiml_measure_is_error.Rd
utiml/man/print.EBRmodel.Rd
utiml/man/print.RPCmodel.Rd
utiml/man/rpc.Rd
utiml/man/print.LIFTmodel.Rd
utiml/man/print.PSmodel.Rd
utiml/man/predict.LPmodel.Rd
utiml/man/as.mlresult.Rd
utiml/man/predict.BRPmodel.Rd
utiml/man/mbr.Rd
utiml/man/print.kFoldPartition.Rd
utiml/man/utiml_measure_macro_accuracy.Rd
utiml/man/predict.NSmodel.Rd
utiml/man/ecc.Rd
utiml/man/mcut_threshold.Rd
utiml/man/ebr.Rd
utiml/man/utiml_lapply.Rd
utiml/man/print.mlconfmat.Rd
utiml/man/utiml_measure_precision.Rd
utiml/man/as.matrix.mlresult.Rd
utiml/man/utiml_measure_macro_AUC.Rd
utiml/man/print.RDBRmodel.Rd
utiml/man/remove_unlabeled_instances.Rd
utiml/man/utiml_random_split.Rd
utiml/man/predict.LIFTmodel.Rd
utiml/man/utiml_measure_micro_accuracy.Rd
utiml/man/utiml_ensemble_check_voteschema.Rd
utiml/man/predict.PruDentmodel.Rd
utiml/man/utiml_measure_micro_precision.Rd
utiml/man/rakel.Rd
utiml/man/print.majorityModel.Rd
utiml/man/normalize_mldata.Rd
utiml/man/merge_mlconfmat.Rd
utiml/man/predict.DBRmodel.Rd
utiml/man/dbr.Rd
utiml/man/utiml_measure_micro_AUC.Rd
utiml/man/utiml_stratified_split.Rd
utiml/man/utiml_ensemble_maximum.Rd
utiml/man/baseline.Rd
utiml/man/is.probability.Rd
utiml/man/utiml_measure_ranking_loss.Rd
utiml/man/mltrain.Rd
utiml/man/lp.Rd
utiml/man/predict.RPCmodel.Rd
utiml/man/brplus.Rd
utiml/man/print.EPSmodel.Rd
utiml/man/utiml_measure_names.Rd
utiml/man/utiml_predict_binary_ensemble.Rd
utiml/man/print.CCmodel.Rd
utiml/man/subset_correction.Rd
utiml/man/predict.MBRmodel.Rd
utiml/man/utiml_measure_micro_f1.Rd
utiml/man/create_kfold_partition.Rd
utiml/man/create_subset.Rd
utiml/man/print.MBRmodel.Rd
utiml/man/ps.Rd
utiml/man/remove_attributes.Rd
utiml/man/utiml_measure_recall.Rd
utiml/man/summary.mltransformation.Rd
utiml/man/print.randomModel.Rd
utiml/man/create_random_subset.Rd
utiml/man/clr.Rd
utiml/man/print.DBRmodel.Rd
utiml/man/utiml_measure_one_error.Rd
utiml/man/as.probability.Rd
utiml/man/scut_threshold.Rd
utiml/man/utiml_measure_macro_precision.Rd
utiml/man/utiml_newdata.Rd
utiml/man/utiml_measure_micro_recall.Rd
utiml/man/utiml_measure_binary_accuracy.Rd
utiml/man/mlpredict.Rd
utiml/man/utiml_ensemble_method.Rd
utiml/man/toyml.Rd
utiml/man/fill_sparce_mldata.Rd
utiml/man/utiml_ifelse.Rd
utiml/man/ctrl.Rd
utiml/man/predict.HOMERmodel.Rd
utiml/man/ppt.Rd
utiml/man/predict.BASELINEmodel.Rd
utiml/man/replace_nominal_attributes.Rd
utiml/man/mldata.Rd
utiml/man/multilabel_measures.Rd
utiml/man/predict.ECCmodel.Rd
utiml/man/ns.Rd
utiml/man/fixed_threshold.Rd
utiml/man/is.bipartition.Rd
utiml/man/remove_skewness_labels.Rd
utiml/man/predict.CTRLmodel.Rd
utiml/man/multilabel_confusion_matrix.Rd
utiml/man/utiml_measure_hamming_loss.Rd
utiml/man/predict.CLRmodel.Rd
utiml/man/print.RAkELmodel.Rd
utiml/man/print.PPTmodel.Rd
utiml/man/print.mlresult.Rd
utiml/man/predict.EBRmodel.Rd
utiml/man/utiml_measure_binary_precision.Rd
utiml/man/utiml_measure_coverage.Rd
utiml/man/utiml_rename.Rd
utiml/man/utiml_measure_macro_f1.Rd
utiml/man/print.BRmodel.Rd
utiml/man/predict.CCmodel.Rd
utiml/man/predict.RDBRmodel.Rd
utiml/man/utiml_measure_ranking_error.Rd
utiml/man/utiml_ensemble_average.Rd
utiml/man/utiml_normalize.Rd
utiml/man/lift.Rd
utiml/man/utiml_measure_accuracy.Rd
utiml/man/utiml_compute_ensemble.Rd
utiml/man/print.PruDentmodel.Rd
utiml/man/utiml_iterative_split.Rd
utiml/man/utiml_ensemble_majority.Rd
utiml/man/multilabel_evaluate.Rd
utiml/man/print.NSmodel.Rd
utiml/man/utiml.Rd
utiml/man/print.CLRmodel.Rd
utiml/man/utiml_all_measures_names.Rd
utiml/man/br.Rd
utiml/man/utiml_preserve_seed.Rd
utiml/man/predict.RAkELmodel.Rd
utiml/man/multilabel_prediction.Rd
utiml/man/utiml_measure_f1.Rd
utiml/man/predict.BRmodel.Rd
utiml/man/prudent.Rd
utiml/man/utiml_labels_IG.Rd
utiml/man/utiml_measure_binary_AUC.Rd
utiml/man/plus-.mlconfmat.Rd
utiml/man/utiml_measure_binary_f1.Rd
utiml/man/predict.EPSmodel.Rd
utiml/man/pcut_threshold.Rd
utiml/man/print.LPmodel.Rd
utiml/man/as.bipartition.Rd
utiml/man/utiml_validate_splitmethod.Rd
utiml/man/predict.PPTmodel.Rd
utiml/man/partition_fold.Rd
utiml/man/print.CTRLmodel.Rd
utiml/man/utiml_measure_binary_recall.Rd
utiml/man/as.ranking.Rd
utiml/man/print.BRPmodel.Rd
utiml/man/eps.Rd
utiml/man/utiml_measure_average_precision.Rd
utiml/man/utiml_measure_margin_loss.Rd
utiml/man/rcut_threshold.Rd
utiml/man/cc.Rd
utiml/man/utiml_measure_macro_recall.Rd
utiml/man/utiml_ensemble_minimum.Rd
utiml/man/remove_unique_attributes.Rd
utiml/man/remove_labels.Rd
utiml/man/utiml_restore_seed.Rd
utiml/man/lcard_threshold.Rd
utiml/man/utiml_labels_correlation.Rd
utiml/man/utiml_measure_subset_accuracy.Rd
utiml/man/sub-.mlresult.Rd
utiml/man/rdbr.Rd
utiml/man/predict.PSmodel.Rd
utiml/man/utiml_is_equal_sets.Rd
utiml/man/print.ECCmodel.Rd
utiml/man/compute_multilabel_predictions.Rd
utiml/man/homer.Rd
utiml/LICENSE