Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents Article Swipe
Minwoo Kim
,
Jin Hee Bae
,
Bohyun Wang
,
Hansol Ko
,
Joon S. Lim
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s23010098
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s23010098
In this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents decide whether to select a feature. Main agents select the optimal features, and guide agents present the criteria for judging the main agents’ actions. After obtaining the main and guide rewards for the features selected by the agents, the main agent that behaves differently from the guide agent updates their Q-values by calculating the learning reward delivered to the main agents. The behavior comparison helps the main agent decide whether its own behavior is correct, without using other algorithms. After performing this process for each episode, the features are finally selected. The feature selection method proposed in this study uses multiple agents, reducing the number of actions each agent can perform and finding optimal features effectively and quickly. Finally, comparative experimental results on multiple datasets show that the proposed method can select effective features for classification and increase classification accuracy.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23010098
- https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321
- OA Status
- gold
- Cited By
- 6
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312100155
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4312100155Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23010098Digital Object Identifier
- Title
-
Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide AgentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-22Full publication date if available
- Authors
-
Minwoo Kim, Jin Hee Bae, Bohyun Wang, Hansol Ko, Joon S. LimList of authors in order
- Landing page
-
https://doi.org/10.3390/s23010098Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321Direct OA link when available
- Concepts
-
Reinforcement learning, Feature selection, Computer science, Artificial intelligence, Selection (genetic algorithm), Feature (linguistics), Multi-agent system, Reinforcement, Machine learning, Engineering, Philosophy, Linguistics, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4312100155 |
|---|---|
| doi | https://doi.org/10.3390/s23010098 |
| ids.doi | https://doi.org/10.3390/s23010098 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/36616694 |
| ids.openalex | https://openalex.org/W4312100155 |
| fwci | 0.74275081 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D007858 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Learning |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000465 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Algorithms |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D012201 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Reward |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D007858 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Learning |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000465 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Algorithms |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D012201 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Reward |
| type | article |
| title | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| awards[0].id | https://openalex.org/G778820181 |
| awards[0].funder_id | https://openalex.org/F4320322120 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2020R1I1A1A01066599 |
| awards[0].funder_display_name | National Research Foundation of Korea |
| biblio.issue | 1 |
| biblio.volume | 23 |
| biblio.last_page | 98 |
| biblio.first_page | 98 |
| topics[0].id | https://openalex.org/T10057 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9961000084877014 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Face and Expression Recognition |
| topics[1].id | https://openalex.org/T11652 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.974399983882904 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Imbalanced Data Classification Techniques |
| topics[2].id | https://openalex.org/T12535 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.968999981880188 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Machine Learning and Data Classification |
| funders[0].id | https://openalex.org/F4320322120 |
| funders[0].ror | https://ror.org/013aysd81 |
| funders[0].display_name | National Research Foundation of Korea |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C97541855 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7693455219268799 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[0].display_name | Reinforcement learning |
| concepts[1].id | https://openalex.org/C148483581 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6386513113975525 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q446488 |
| concepts[1].display_name | Feature selection |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6234758496284485 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5588708519935608 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C81917197 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5381885766983032 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q628760 |
| concepts[4].display_name | Selection (genetic algorithm) |
| concepts[5].id | https://openalex.org/C2776401178 |
| concepts[5].level | 2 |
| concepts[5].score | 0.506166398525238 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[5].display_name | Feature (linguistics) |
| concepts[6].id | https://openalex.org/C41550386 |
| concepts[6].level | 2 |
| concepts[6].score | 0.45337364077568054 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q529909 |
| concepts[6].display_name | Multi-agent system |
| concepts[7].id | https://openalex.org/C67203356 |
| concepts[7].level | 2 |
| concepts[7].score | 0.45030486583709717 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1321905 |
| concepts[7].display_name | Reinforcement |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4285084903240204 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C127413603 |
| concepts[9].level | 0 |
| concepts[9].score | 0.25940626859664917 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[9].display_name | Engineering |
| concepts[10].id | https://openalex.org/C138885662 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[10].display_name | Philosophy |
| concepts[11].id | https://openalex.org/C41895202 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[11].display_name | Linguistics |
| concepts[12].id | https://openalex.org/C66938386 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[12].display_name | Structural engineering |
| keywords[0].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[0].score | 0.7693455219268799 |
| keywords[0].display_name | Reinforcement learning |
| keywords[1].id | https://openalex.org/keywords/feature-selection |
| keywords[1].score | 0.6386513113975525 |
| keywords[1].display_name | Feature selection |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6234758496284485 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5588708519935608 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/selection |
| keywords[4].score | 0.5381885766983032 |
| keywords[4].display_name | Selection (genetic algorithm) |
| keywords[5].id | https://openalex.org/keywords/feature |
| keywords[5].score | 0.506166398525238 |
| keywords[5].display_name | Feature (linguistics) |
| keywords[6].id | https://openalex.org/keywords/multi-agent-system |
| keywords[6].score | 0.45337364077568054 |
| keywords[6].display_name | Multi-agent system |
| keywords[7].id | https://openalex.org/keywords/reinforcement |
| keywords[7].score | 0.45030486583709717 |
| keywords[7].display_name | Reinforcement |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.4285084903240204 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/engineering |
| keywords[9].score | 0.25940626859664917 |
| keywords[9].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.3390/s23010098 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s23010098 |
| locations[1].id | pmid:36616694 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/36616694 |
| locations[2].id | pmh:oai:doaj.org/article:4883930de37a4f95a01cf1aa3bde20f1 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 23, Iss 1, p 98 (2022) |
| locations[2].landing_page_url | https://doaj.org/article/4883930de37a4f95a01cf1aa3bde20f1 |
| locations[3].id | pmh:oai:mdpi.com:/1424-8220/23/1/98/ |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400947 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | MDPI (MDPI AG) |
| locations[3].source.host_organization | https://openalex.org/I4210097602 |
| locations[3].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors; Volume 23; Issue 1; Pages: 98 |
| locations[3].landing_page_url | https://dx.doi.org/10.3390/s23010098 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:9823489 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sensors (Basel) |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9823489 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5025054154 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8133-8449 |
| authorships[0].author.display_name | Minwoo Kim |
| authorships[0].countries | KR |
| authorships[0].affiliations[0].raw_affiliation_string | AI Team, 2nd R&D Center, MEZOO Co., Ltd., Gieopdosi-ro 200, Jijeong-myeon, Wonju-si 26354, Gangwon-do, Republic of Korea |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I12832649 |
| authorships[0].affiliations[1].raw_affiliation_string | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[0].institutions[0].id | https://openalex.org/I12832649 |
| authorships[0].institutions[0].ror | https://ror.org/03ryywt80 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I12832649 |
| authorships[0].institutions[0].country_code | KR |
| authorships[0].institutions[0].display_name | Gachon University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Minwoo Kim |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | AI Team, 2nd R&D Center, MEZOO Co., Ltd., Gieopdosi-ro 200, Jijeong-myeon, Wonju-si 26354, Gangwon-do, Republic of Korea, Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[1].author.id | https://openalex.org/A5055127357 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1694-6373 |
| authorships[1].author.display_name | Jin Hee Bae |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1174212 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA |
| authorships[1].institutions[0].id | https://openalex.org/I1174212 |
| authorships[1].institutions[0].ror | https://ror.org/03taz7m60 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I1174212 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Southern California |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jinhee Bae |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA |
| authorships[2].author.id | https://openalex.org/A5053186784 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3112-2644 |
| authorships[2].author.display_name | Bohyun Wang |
| authorships[2].countries | KR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I12832649 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[2].institutions[0].id | https://openalex.org/I12832649 |
| authorships[2].institutions[0].ror | https://ror.org/03ryywt80 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I12832649 |
| authorships[2].institutions[0].country_code | KR |
| authorships[2].institutions[0].display_name | Gachon University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Bohyun Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[3].author.id | https://openalex.org/A5015860209 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Hansol Ko |
| authorships[3].countries | KR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I12832649 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[3].institutions[0].id | https://openalex.org/I12832649 |
| authorships[3].institutions[0].ror | https://ror.org/03ryywt80 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I12832649 |
| authorships[3].institutions[0].country_code | KR |
| authorships[3].institutions[0].display_name | Gachon University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hansol Ko |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[4].author.id | https://openalex.org/A5102803771 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0018-946X |
| authorships[4].author.display_name | Joon S. Lim |
| authorships[4].countries | KR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I12832649 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| authorships[4].institutions[0].id | https://openalex.org/I12832649 |
| authorships[4].institutions[0].ror | https://ror.org/03ryywt80 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I12832649 |
| authorships[4].institutions[0].country_code | KR |
| authorships[4].institutions[0].display_name | Gachon University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Joon S. Lim |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of Korea |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10057 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9961000084877014 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Face and Expression Recognition |
| related_works | https://openalex.org/W4310083477, https://openalex.org/W2328553770, https://openalex.org/W2920061524, https://openalex.org/W1977959518, https://openalex.org/W2038908348, https://openalex.org/W2107890255, https://openalex.org/W2106552856, https://openalex.org/W2145821588, https://openalex.org/W4386564352, https://openalex.org/W2952668426 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/s23010098 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s23010098 |
| primary_location.id | doi:10.3390/s23010098 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8220/23/1/98/pdf?version=1672132321 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s23010098 |
| publication_date | 2022-12-22 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2979417040, https://openalex.org/W2810596787, https://openalex.org/W2948646149, https://openalex.org/W7066667914, https://openalex.org/W2163891305, https://openalex.org/W2791315675, https://openalex.org/W2154053567, https://openalex.org/W4250863246, https://openalex.org/W2014915963, https://openalex.org/W6754881864, https://openalex.org/W2906922093, https://openalex.org/W6603216745, https://openalex.org/W2123636872, https://openalex.org/W2168825483, https://openalex.org/W2938321354, https://openalex.org/W3100366369, https://openalex.org/W2883971393, https://openalex.org/W2921715603, https://openalex.org/W3108439758, https://openalex.org/W6752059867, https://openalex.org/W2885467852, https://openalex.org/W2953043480, https://openalex.org/W3126977517, https://openalex.org/W2759903677, https://openalex.org/W1975777241, https://openalex.org/W41159429, https://openalex.org/W6788247690, https://openalex.org/W2087684630, https://openalex.org/W2963390684, https://openalex.org/W78766501, https://openalex.org/W2998216295, https://openalex.org/W2891385203 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 5, 12, 22, 30, 53 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.by | 84, 100 |
| abstract_inverted_index.in | 145 |
| abstract_inverted_index.is | 122 |
| abstract_inverted_index.of | 35, 40, 154 |
| abstract_inverted_index.on | 171 |
| abstract_inverted_index.or | 19 |
| abstract_inverted_index.to | 7, 51, 106 |
| abstract_inverted_index.we | 3 |
| abstract_inverted_index.The | 110, 140 |
| abstract_inverted_index.and | 29, 43, 46, 61, 77, 160, 165, 185 |
| abstract_inverted_index.are | 15, 137 |
| abstract_inverted_index.can | 158, 179 |
| abstract_inverted_index.for | 17, 67, 80, 132, 183 |
| abstract_inverted_index.has | 38 |
| abstract_inverted_index.its | 119 |
| abstract_inverted_index.new | 23 |
| abstract_inverted_index.one | 39 |
| abstract_inverted_index.own | 120 |
| abstract_inverted_index.the | 36, 41, 58, 65, 69, 75, 81, 85, 87, 94, 102, 107, 114, 135, 152, 176 |
| abstract_inverted_index.Each | 33 |
| abstract_inverted_index.Main | 55 |
| abstract_inverted_index.each | 133, 156 |
| abstract_inverted_index.find | 9 |
| abstract_inverted_index.from | 11, 93 |
| abstract_inverted_index.main | 42, 70, 76, 88, 108, 115 |
| abstract_inverted_index.show | 174 |
| abstract_inverted_index.that | 14, 90, 175 |
| abstract_inverted_index.this | 1, 130, 146 |
| abstract_inverted_index.uses | 148 |
| abstract_inverted_index.After | 73, 128 |
| abstract_inverted_index.agent | 89, 96, 116, 157 |
| abstract_inverted_index.guide | 31, 44, 62, 78, 95 |
| abstract_inverted_index.helps | 113 |
| abstract_inverted_index.other | 126 |
| abstract_inverted_index.study | 147 |
| abstract_inverted_index.their | 98 |
| abstract_inverted_index.these | 47 |
| abstract_inverted_index.using | 21, 125 |
| abstract_inverted_index.agent. | 32 |
| abstract_inverted_index.agents | 48, 56, 63 |
| abstract_inverted_index.called | 25 |
| abstract_inverted_index.decide | 49, 117 |
| abstract_inverted_index.method | 6, 24, 143, 178 |
| abstract_inverted_index.number | 153 |
| abstract_inverted_index.reward | 104 |
| abstract_inverted_index.select | 52, 57, 180 |
| abstract_inverted_index.study, | 2 |
| abstract_inverted_index.actions | 155 |
| abstract_inverted_index.agents, | 45, 86, 150 |
| abstract_inverted_index.agents. | 109 |
| abstract_inverted_index.behaves | 91 |
| abstract_inverted_index.dataset | 13, 37 |
| abstract_inverted_index.feature | 34, 141 |
| abstract_inverted_index.finally | 138 |
| abstract_inverted_index.finding | 161 |
| abstract_inverted_index.judging | 68 |
| abstract_inverted_index.optimal | 59, 162 |
| abstract_inverted_index.perform | 159 |
| abstract_inverted_index.present | 64 |
| abstract_inverted_index.process | 131 |
| abstract_inverted_index.propose | 4 |
| abstract_inverted_index.results | 170 |
| abstract_inverted_index.rewards | 79 |
| abstract_inverted_index.updates | 97 |
| abstract_inverted_index.whether | 50, 118 |
| abstract_inverted_index.without | 124 |
| abstract_inverted_index.Finally, | 167 |
| abstract_inverted_index.Q-values | 99 |
| abstract_inverted_index.actions. | 72 |
| abstract_inverted_index.behavior | 111, 121 |
| abstract_inverted_index.correct, | 123 |
| abstract_inverted_index.criteria | 66 |
| abstract_inverted_index.datasets | 173 |
| abstract_inverted_index.episode, | 134 |
| abstract_inverted_index.feature. | 54 |
| abstract_inverted_index.features | 10, 82, 136, 163, 182 |
| abstract_inverted_index.increase | 186 |
| abstract_inverted_index.learning | 28, 103 |
| abstract_inverted_index.multiple | 149, 172 |
| abstract_inverted_index.proposed | 144, 177 |
| abstract_inverted_index.quickly. | 166 |
| abstract_inverted_index.reducing | 151 |
| abstract_inverted_index.selected | 83 |
| abstract_inverted_index.accuracy. | 188 |
| abstract_inverted_index.agents’ | 71 |
| abstract_inverted_index.delivered | 105 |
| abstract_inverted_index.effective | 16, 181 |
| abstract_inverted_index.features, | 60 |
| abstract_inverted_index.obtaining | 74 |
| abstract_inverted_index.selected. | 139 |
| abstract_inverted_index.selection | 142 |
| abstract_inverted_index.comparison | 112 |
| abstract_inverted_index.performing | 129 |
| abstract_inverted_index.algorithms. | 127 |
| abstract_inverted_index.calculating | 101 |
| abstract_inverted_index.comparative | 168 |
| abstract_inverted_index.differently | 92 |
| abstract_inverted_index.effectively | 164 |
| abstract_inverted_index.multi-agent | 26 |
| abstract_inverted_index.prediction, | 20 |
| abstract_inverted_index.experimental | 169 |
| abstract_inverted_index.automatically | 8 |
| abstract_inverted_index.reinforcement | 27 |
| abstract_inverted_index.classification | 18, 184, 187 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5102803771 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I12832649 |
| citation_normalized_percentile.value | 0.70266438 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |