utiml: Utilities for Multi-Label Learning

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

AuthorAdriano Rivolli [aut, cre]
Date of publication2016-11-19 21:28:00
MaintainerAdriano Rivolli <rivolli@utfpr.edu.br>
LicenseGPL | file LICENSE
Version0.1.1
https://github.com/rivolli/utiml

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

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