utiml

Utilities for multi-label learning

https://github.com/rivolli/utiml

Science Score: 23.0%

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Keywords from Contributors

characterization-measures meta-features meta-learning mtl

Last synced: 11 months ago · JSON representation

Repository

Utilities for multi-label learning

Basic Info

Host: GitHub
Owner: rivolli
Language: R
Default Branch: master
Size: 1.35 MB

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Stars: 28
Watchers: 6
Forks: 7
Open Issues: 5
Releases: 0

Created almost 11 years ago · Last pushed over 4 years ago

Metadata Files

Readme

utiml: Utilities for Multi-label Learning

The utiml package is a framework to support multi-label processing, like Mulan on Weka.

The main methods available on this package are organized in the groups: - Classification methods - Evaluation methods - Pre-process utilities - Sampling methods - Threshold methods

Instalation

The installation process is similar to other packages available on CRAN: r install.packages("utiml")

This will also install mldr. To run the examples in this document, you also need to install the packages: ```r

Base classifiers (SVM and Random Forest)

install.packages(c("e1071", "randomForest")) ```

Install via github (development version)

r devtools::install_github("rivolli/utiml")

Multi-label Classification

Running Binary Relevance Method

```{r} library(utiml)

Create two partitions (train and test) of toyml multi-label dataset

ds <- createholdoutpartition(toyml, c(train=0.65, test=0.35))

Create a Binary Relevance Model using e1071::svm method

brmodel <- br(ds$train, "SVM", seed=123)

Predict

prediction <- predict(brmodel, ds$test)

Show the predictions

head(as.bipartition(prediction)) head(as.ranking(prediction))

Apply a threshold

newpred <- rcut_threshold(prediction, 2)

Evaluate the models

result <- multilabelevaluate(ds$tes, prediction, "bipartition") thresres <- multilabelevaluate(ds$tes, newpred, "bipartition")

Print the result

print(round(cbind(Default=result, RCUT=thresres), 3)) ```

Running Ensemble of Classifier Chains

```{r} library(utiml)

Create three partitions (train, val, test) of emotions dataset

partitions <- c(train = 0.6, val = 0.2, test = 0.2) ds <- createholdoutpartition(emotions, partitions, method="iterative")

Create an Ensemble of Classifier Chains using Random Forest (randomForest package)

eccmodel <- ecc(ds$train, "RF", m=3, cores=parallel::detectCores(), seed=123)

Predict

val <- predict(eccmodel, ds$val, cores=parallel::detectCores()) test <- predict(eccmodel, ds$test, cores=parallel::detectCores())

Apply a threshold

thresholds <- scutthreshold(val, ds$val, cores=parallel::detectCores()) new.val <- fixedthreshold(val, thresholds) new.test <- fixed_threshold(test, thresholds)

Evaluate the models

measures <- c("subset-accuracy", "F1", "hamming-loss", "macro-based")

result <- cbind( Test = multilabelevaluate(ds$tes, test, measures), TestWithThreshold = multilabelevaluate(ds$tes, new.test, measures), Validation = multilabelevaluate(ds$val, val, measures), ValidationWithThreshold = multilabelevaluate(ds$val, new.val, measures) )

print(round(result, 3)) ```

More examples and details are available on functions documentations and vignettes, please refer to the documentation.

How to cite?

@article{RJ-2018-041, author = {Adriano Rivolli and Andre C. P. L. F. de Carvalho}, title = {{The utiml Package: Multi-label Classification in R}}, year = {2018}, journal = {{The R Journal}}, doi = {10.32614/RJ-2018-041}, url = {https://doi.org/10.32614/RJ-2018-041}, pages = {24--37}, volume = {10}, number = {2} }

Owner

Name: Adriano Rivolli
Login: rivolli
Kind: user

Repositories: 3
Profile: https://github.com/rivolli

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Development Distribution Score (DDS): 0.0

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Name	Email	Commits
Adriano Rivolli	r**i@g**m	212
Adriano Rivolli	r**i@u**r	41
Adriano Rivolli	r****i	5
Adriano	a**o@f**z	2
JS Liu	j**n@j**a	2
mxhm	h**s@p**u	1
Jason Liu	1****s	1
Adriano Rivolli	r**i@b**m	1

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rivolli (2)
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Total packages: 1
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- cran 267 last-month

Total dependent packages: 0
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Total versions: 8
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cran.r-project.org: utiml

Utilities for Multi-Label Learning

Homepage: https://github.com/rivolli/utiml
Documentation: http://cran.r-project.org/web/packages/utiml/utiml.pdf
License: GPL-3
Status: removed
Latest release: 0.1.7
published about 5 years ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 267 Last month

Rankings

Forks count: 9.1%

Stargazers count: 10.4%

Average: 24.2%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Downloads: 36.1%

Maintainers (1)

rivolli@utfpr.edu.br

Last synced: about 1 year ago

Dependencies

DESCRIPTION cran

R >= 3.0.0 depends
ROCR * depends
mldr >= 0.4.0 depends
parallel * depends
methods * imports
stats * imports
utils * imports
C50 * suggests
e1071 * suggests
infotheo * suggests
kknn * suggests
knitr * suggests
markdown * suggests
randomForest * suggests
rmarkdown * suggests
rpart * suggests
testthat * suggests
xgboost >= 0.6 suggests

utiml

Science Score: 23.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

utiml: Utilities for Multi-label Learning

Instalation

Base classifiers (SVM and Random Forest)

Install via github (development version)

Multi-label Classification

Running Binary Relevance Method

Create two partitions (train and test) of toyml multi-label dataset

Create a Binary Relevance Model using e1071::svm method

Predict

Show the predictions

Apply a threshold

Evaluate the models

Print the result

Running Ensemble of Classifier Chains

Create three partitions (train, val, test) of emotions dataset

Create an Ensemble of Classifier Chains using Random Forest (randomForest package)

Predict

Apply a threshold

Evaluate the models

How to cite?

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