Multilabel feature selection: A comprehensive review and guiding experiments
Sign Up to like & getrecommendations! Published in 2018 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1240
Abstract: Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods… read more here.
Keywords: comprehensive review; selection; feature selection; multilabel feature ... See more keywords
Multilabel Feature Selection Using Relief and Minimum Redundancy Maximum Relevance Based on Neighborhood Rough Sets
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2982536
Abstract: Recently, multilabel classification is of increasing interest in machine learning and artificial intelligence. However, the distances of samples in most Relief methods easily result in heterogeneous or similar samples abnormal when the distances are very… read more here.
Keywords: based neighborhood; multilabel feature; relief minimum; feature selection ... See more keywords
Global-Guided Label-Aware Adaptive Multilabel Feature Selection via Fuzzy Mutual Information
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2025.3613499
Abstract: In recent years, embedded-based multilabel feature selection (MFS) has received increasing attention due to its ability to perform both feature selection and model training simultaneously. However, most existing methods tend to overlook the varying importance… read more here.
Keywords: information; feature selection; feature; selection ... See more keywords
Multilabel Feature Selection: A Local Causal Structure Learning Approach.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3111288
Abstract: Multilabel feature selection plays an essential role in high-dimensional multilabel learning tasks. Existing multilabel feature selection approaches mainly either explore the feature-label and feature-feature correlations or the label-label and feature-feature correlations. A few of them… read more here.
Keywords: multilabel feature; feature selection; feature; causal ... See more keywords
Fast Multilabel Feature Selection via Global Relevance and Redundancy Optimization.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3208956
Abstract: Information theoretical-based methods have attracted a great attention in recent years and gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the existing methods consider a heuristic way to the grid search of… read more here.
Keywords: redundancy; relevance; multilabel feature; optimization ... See more keywords
Memetic multilabel feature selection using pruned refinement process
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Big Data"
DOI: 10.1186/s40537-024-00961-2
Abstract: With the growing complexity of data structures, which include high-dimensional and multilabel datasets, the significance of feature selection has become more emphasized. Multilabel feature selection endeavors to identify a subset of features that concurrently exhibit… read more here.
Keywords: feature; process; multilabel feature; feature selection ... See more keywords