View source: R/ensemble_fselect.R
ensemble_fselect | R Documentation |
Ensemble feature selection using multiple learners. The ensemble feature selection method is designed to identify the most informative features from a given dataset by leveraging multiple machine learning models and resampling techniques. Returns an EnsembleFSResult.
ensemble_fselect(
fselector,
task,
learners,
init_resampling,
inner_resampling,
measure,
terminator,
callbacks = NULL,
store_benchmark_result = TRUE,
store_models = TRUE
)
fselector |
(FSelector) |
task |
(mlr3::Task) |
learners |
(list of mlr3::Learner) |
init_resampling |
(mlr3::Resampling) |
inner_resampling |
(mlr3::Resampling) |
measure |
(mlr3::Measure) |
terminator |
(bbotk::Terminator) |
callbacks |
(list of lists of CallbackBatchFSelect) |
store_benchmark_result |
( |
store_models |
( |
The method begins by applying an initial resampling technique specified by the user, to create multiple subsamples from the original dataset. This resampling process helps in generating diverse subsets of data for robust feature selection.
For each subsample generated in the previous step, the method performs wrapped-based feature selection (auto_fselector) using each provided learner, the given inner resampling method, performance measure and optimization algorithm. This process generates the best feature subset for each combination of subsample and learner. Results are stored in an EnsembleFSResult.
an EnsembleFSResult object.
Saeys, Yvan, Abeel, Thomas, Van De Peer, Yves (2008). “Robust feature selection using ensemble feature selection techniques.” Machine Learning and Knowledge Discovery in Databases, 5212 LNAI, 313–325. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-540-87481-2_21")}.
Abeel, Thomas, Helleputte, Thibault, Van de Peer, Yves, Dupont, Pierre, Saeys, Yvan (2010). “Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.” Bioinformatics, 26, 392–398. ISSN 1367-4803, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/BIOINFORMATICS/BTP630")}.
Pes, Barbara (2020). “Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains.” Neural Computing and Applications, 32(10), 5951–5973. ISSN 14333058, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00521-019-04082-3")}.
efsr = ensemble_fselect(
fselector = fs("random_search"),
task = tsk("sonar"),
learners = lrns(c("classif.rpart", "classif.featureless")),
init_resampling = rsmp("subsampling", repeats = 2),
inner_resampling = rsmp("cv", folds = 3),
measure = msr("classif.ce"),
terminator = trm("evals", n_evals = 10)
)
efsr
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