Bird’s Eye View feature selection for high-dimensional data
<p dir="ltr">In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this chal...
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2023
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| _version_ | 1864513520772579328 |
|---|---|
| author | Samir Brahim Belhaouari (16855434) |
| author2 | Mohammed Bilal Shakeel (19438030) Aiman Erbad (14150589) Zarina Oflaz (14609956) Khelil Kassoul (18441114) |
| author2_role | author author author author |
| author_facet | Samir Brahim Belhaouari (16855434) Mohammed Bilal Shakeel (19438030) Aiman Erbad (14150589) Zarina Oflaz (14609956) Khelil Kassoul (18441114) |
| author_role | author |
| dc.creator.none.fl_str_mv | Samir Brahim Belhaouari (16855434) Mohammed Bilal Shakeel (19438030) Aiman Erbad (14150589) Zarina Oflaz (14609956) Khelil Kassoul (18441114) |
| dc.date.none.fl_str_mv | 2023-08-16T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-023-39790-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Bird_s_Eye_View_feature_selection_for_high-dimensional_data/26772193 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Machine Learning Feature Selection High Dimensional Data Bird's Eye View (BEV) Evolutionary Algorithms Genetic Algorithm Reinforcement Learning |
| dc.title.none.fl_str_mv | Bird’s Eye View feature selection for high-dimensional data |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-39790-3" target="_blank">https://dx.doi.org/10.1038/s41598-023-39790-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9b362b1aa05b0bd177e4b086746e0f62 |
| identifier_str_mv | 10.1038/s41598-023-39790-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26772193 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bird’s Eye View feature selection for high-dimensional dataSamir Brahim Belhaouari (16855434)Mohammed Bilal Shakeel (19438030)Aiman Erbad (14150589)Zarina Oflaz (14609956)Khelil Kassoul (18441114)Information and computing sciencesArtificial intelligenceMachine learningMachine LearningFeature SelectionHigh Dimensional DataBird's Eye View (BEV)Evolutionary AlgorithmsGenetic AlgorithmReinforcement Learning<p dir="ltr">In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-39790-3" target="_blank">https://dx.doi.org/10.1038/s41598-023-39790-3</a></p>2023-08-16T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-023-39790-3https://figshare.com/articles/journal_contribution/Bird_s_Eye_View_feature_selection_for_high-dimensional_data/26772193CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267721932023-08-16T09:00:00Z |
| spellingShingle | Bird’s Eye View feature selection for high-dimensional data Samir Brahim Belhaouari (16855434) Information and computing sciences Artificial intelligence Machine learning Machine Learning Feature Selection High Dimensional Data Bird's Eye View (BEV) Evolutionary Algorithms Genetic Algorithm Reinforcement Learning |
| status_str | publishedVersion |
| title | Bird’s Eye View feature selection for high-dimensional data |
| title_full | Bird’s Eye View feature selection for high-dimensional data |
| title_fullStr | Bird’s Eye View feature selection for high-dimensional data |
| title_full_unstemmed | Bird’s Eye View feature selection for high-dimensional data |
| title_short | Bird’s Eye View feature selection for high-dimensional data |
| title_sort | Bird’s Eye View feature selection for high-dimensional data |
| topic | Information and computing sciences Artificial intelligence Machine learning Machine Learning Feature Selection High Dimensional Data Bird's Eye View (BEV) Evolutionary Algorithms Genetic Algorithm Reinforcement Learning |