Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition Article Swipe
Shoutao Li
,
Ruyi Qu
,
Yu Zhang
,
Dingli Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2023.036999
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2023.036999
Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and considerable limitations of a single classifier. Finally, the hyperparam-eters of the ensemble classifier are optimized by Bayesian optimization, and the experiment proves that the RUSBoost algorithm can complete the gait recognition task well. Compared with the Adaboost, Tomeklinks-Adaboost and ROS-Adaboost ensemble algorithms, the RUSBoost ensemble algorithm can complete the FOG recognition task more efficiently. When the maximum number of splits is 1023, and the number of base classifiers is 100, the performance of the RUSBoost ensemble algorithm can reach the best. The accuracy of the time recognition algorithm was 87.8%, the sensitivity was 89.7%, and the specificity was 87.5%.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/iasc.2023.036999
- https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdf
- OA Status
- hybrid
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386494302
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386494302Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/iasc.2023.036999Digital Object Identifier
- Title
-
Research on Freezing of Gait Recognition Method Based on Variational Mode DecompositionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Shoutao Li, Ruyi Qu, Yu Zhang, Dingli YuList of authors in order
- Landing page
-
https://doi.org/10.32604/iasc.2023.036999Publisher landing page
- PDF URL
-
https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdfDirect OA link when available
- Concepts
-
Computer science, AdaBoost, Classifier (UML), Artificial intelligence, Pattern recognition (psychology), Ensemble learning, Gait, Hyperparameter, Algorithm, Machine learning, Physiology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386494302 |
|---|---|
| doi | https://doi.org/10.32604/iasc.2023.036999 |
| ids.doi | https://doi.org/10.32604/iasc.2023.036999 |
| ids.openalex | https://openalex.org/W4386494302 |
| fwci | 0.0 |
| type | article |
| title | Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition |
| biblio.issue | 3 |
| biblio.volume | 37 |
| biblio.last_page | 2823 |
| biblio.first_page | 2809 |
| topics[0].id | https://openalex.org/T12740 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9750000238418579 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Gait Recognition and Analysis |
| funders[0].id | https://openalex.org/F4320326884 |
| funders[0].ror | |
| funders[0].display_name | State Key Laboratory of Automotive Simulation and Control |
| funders[1].id | https://openalex.org/F4320326926 |
| funders[1].ror | |
| funders[1].display_name | State Key Laboratory of Control and Simulation of Power System and Generation Equipment |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7982144355773926 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C141404830 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7292319536209106 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2823869 |
| concepts[1].display_name | AdaBoost |
| concepts[2].id | https://openalex.org/C95623464 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6583670973777771 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[2].display_name | Classifier (UML) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6517229080200195 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6429885029792786 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C45942800 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4485020041465759 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q245652 |
| concepts[5].display_name | Ensemble learning |
| concepts[6].id | https://openalex.org/C151800584 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4123859405517578 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2370000 |
| concepts[6].display_name | Gait |
| concepts[7].id | https://openalex.org/C8642999 |
| concepts[7].level | 2 |
| concepts[7].score | 0.41110119223594666 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q4171168 |
| concepts[7].display_name | Hyperparameter |
| concepts[8].id | https://openalex.org/C11413529 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3785611689090729 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[8].display_name | Algorithm |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.34601542353630066 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C42407357 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q521 |
| concepts[10].display_name | Physiology |
| concepts[11].id | https://openalex.org/C86803240 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[11].display_name | Biology |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7982144355773926 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/adaboost |
| keywords[1].score | 0.7292319536209106 |
| keywords[1].display_name | AdaBoost |
| keywords[2].id | https://openalex.org/keywords/classifier |
| keywords[2].score | 0.6583670973777771 |
| keywords[2].display_name | Classifier (UML) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6517229080200195 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.6429885029792786 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/ensemble-learning |
| keywords[5].score | 0.4485020041465759 |
| keywords[5].display_name | Ensemble learning |
| keywords[6].id | https://openalex.org/keywords/gait |
| keywords[6].score | 0.4123859405517578 |
| keywords[6].display_name | Gait |
| keywords[7].id | https://openalex.org/keywords/hyperparameter |
| keywords[7].score | 0.41110119223594666 |
| keywords[7].display_name | Hyperparameter |
| keywords[8].id | https://openalex.org/keywords/algorithm |
| keywords[8].score | 0.3785611689090729 |
| keywords[8].display_name | Algorithm |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.34601542353630066 |
| keywords[9].display_name | Machine learning |
| language | en |
| locations[0].id | doi:10.32604/iasc.2023.036999 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S40639465 |
| locations[0].source.issn | 1079-8587, 2326-005X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1079-8587 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Intelligent Automation & Soft Computing |
| locations[0].source.host_organization | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_name | Taylor & Francis |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_lineage_names | Taylor & Francis |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdf |
| 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 | Intelligent Automation & Soft Computing |
| locations[0].landing_page_url | https://doi.org/10.32604/iasc.2023.036999 |
| locations[1].id | pmh:oai:researchonline.ljmu.ac.uk:23915 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401246 |
| 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 | Liverpool John Moores University |
| locations[1].source.host_organization | https://openalex.org/I63098007 |
| locations[1].source.host_organization_name | Liverpool John Moores University |
| locations[1].source.host_organization_lineage | https://openalex.org/I63098007 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | acceptedVersion |
| locations[1].raw_type | PeerReviewed |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://orcid.org/0000-0003-3177-8942 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5054012309 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7330-0715 |
| authorships[0].author.display_name | Shoutao Li |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I194450716 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Communication Engineering, Jilin University, Changchun, 130012, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I49232843 |
| authorships[0].affiliations[1].raw_affiliation_string | Academy of Electric Information, Changchun University of Architecture and Civil Engineering, Changchun, 130607, China |
| authorships[0].institutions[0].id | https://openalex.org/I49232843 |
| authorships[0].institutions[0].ror | https://ror.org/02an57k10 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I49232843 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Changchun University |
| authorships[0].institutions[1].id | https://openalex.org/I194450716 |
| authorships[0].institutions[1].ror | https://ror.org/00js3aw79 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I194450716 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Jilin University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shoutao Li |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Academy of Electric Information, Changchun University of Architecture and Civil Engineering, Changchun, 130607, China, School of Communication Engineering, Jilin University, Changchun, 130012, China |
| authorships[1].author.id | https://openalex.org/A5111013620 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Ruyi Qu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I194450716 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Communication Engineering, Jilin University, Changchun, 130012, China |
| authorships[1].institutions[0].id | https://openalex.org/I194450716 |
| authorships[1].institutions[0].ror | https://ror.org/00js3aw79 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I194450716 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Jilin University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ruyi Qu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Communication Engineering, Jilin University, Changchun, 130012, China |
| authorships[2].author.id | https://openalex.org/A5100433581 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5848-8892 |
| authorships[2].author.display_name | Yu Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I194450716 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Communication Engineering, Jilin University, Changchun, 130012, China |
| authorships[2].institutions[0].id | https://openalex.org/I194450716 |
| authorships[2].institutions[0].ror | https://ror.org/00js3aw79 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I194450716 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Jilin University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yu Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Communication Engineering, Jilin University, Changchun, 130012, China |
| authorships[3].author.id | https://openalex.org/A5034102301 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3177-8942 |
| authorships[3].author.display_name | Dingli Yu |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I63098007 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Engineering and Technology, Liverpool John Moores University, Liverpool, L33AF, UK |
| authorships[3].institutions[0].id | https://openalex.org/I63098007 |
| authorships[3].institutions[0].ror | https://ror.org/04zfme737 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I63098007 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | Liverpool John Moores University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Dingli Yu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Engineering and Technology, Liverpool John Moores University, Liverpool, L33AF, UK |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12740 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9750000238418579 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Gait Recognition and Analysis |
| related_works | https://openalex.org/W4285046548, https://openalex.org/W2161806676, https://openalex.org/W4226036692, https://openalex.org/W4283697347, https://openalex.org/W4382811895, https://openalex.org/W4298144215, https://openalex.org/W4379930047, https://openalex.org/W4285131761, https://openalex.org/W4375930479, https://openalex.org/W4210825373 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.32604/iasc.2023.036999 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S40639465 |
| best_oa_location.source.issn | 1079-8587, 2326-005X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1079-8587 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Intelligent Automation & Soft Computing |
| best_oa_location.source.host_organization | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_name | Taylor & Francis |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_lineage_names | Taylor & Francis |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdf |
| 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 | Intelligent Automation & Soft Computing |
| best_oa_location.landing_page_url | https://doi.org/10.32604/iasc.2023.036999 |
| primary_location.id | doi:10.32604/iasc.2023.036999 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S40639465 |
| primary_location.source.issn | 1079-8587, 2326-005X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1079-8587 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Intelligent Automation & Soft Computing |
| primary_location.source.host_organization | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_name | Taylor & Francis |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_lineage_names | Taylor & Francis |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://file.techscience.com/files/iasc/2023/TSP_IASC-37-3/TSP_IASC_36999/TSP_IASC_36999.pdf |
| 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 | Intelligent Automation & Soft Computing |
| primary_location.landing_page_url | https://doi.org/10.32604/iasc.2023.036999 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2962908277, https://openalex.org/W3197576140, https://openalex.org/W2114808721, https://openalex.org/W2588989789, https://openalex.org/W2750757685, https://openalex.org/W1965520378, https://openalex.org/W2059852492, https://openalex.org/W4306894664, https://openalex.org/W4210581714, https://openalex.org/W4282583990, https://openalex.org/W2977465880, https://openalex.org/W2511002675, https://openalex.org/W4290755839, https://openalex.org/W4230568085, https://openalex.org/W2171181994, https://openalex.org/W3096719723, https://openalex.org/W2051316215, https://openalex.org/W2791679092, https://openalex.org/W2996303304, https://openalex.org/W2991983284, https://openalex.org/W2071233922, https://openalex.org/W2039520076 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 32, 103 |
| abstract_inverted_index.as | 75 |
| abstract_inverted_index.by | 115 |
| abstract_inverted_index.in | 12 |
| abstract_inverted_index.is | 4, 160, 168 |
| abstract_inverted_index.of | 1, 27, 46, 67, 95, 102, 109, 158, 165, 172, 183 |
| abstract_inverted_index.on | 37 |
| abstract_inverted_index.to | 52, 56, 61, 91 |
| abstract_inverted_index.FOG | 33, 58, 149 |
| abstract_inverted_index.The | 181 |
| abstract_inverted_index.VMD | 44 |
| abstract_inverted_index.and | 8, 24, 65, 79, 99, 118, 138, 162, 194 |
| abstract_inverted_index.are | 113 |
| abstract_inverted_index.can | 126, 146, 177 |
| abstract_inverted_index.the | 5, 21, 38, 47, 57, 63, 68, 72, 76, 81, 87, 93, 107, 110, 119, 123, 128, 135, 142, 148, 155, 163, 170, 173, 179, 184, 190, 195 |
| abstract_inverted_index.use | 71, 86 |
| abstract_inverted_index.was | 188, 192, 197 |
| abstract_inverted_index.100, | 169 |
| abstract_inverted_index.CART | 73 |
| abstract_inverted_index.Gait | 2 |
| abstract_inverted_index.Mode | 40 |
| abstract_inverted_index.Then | 85 |
| abstract_inverted_index.This | 29 |
| abstract_inverted_index.When | 154 |
| abstract_inverted_index.base | 77, 166 |
| abstract_inverted_index.gait | 10, 129 |
| abstract_inverted_index.life | 22 |
| abstract_inverted_index.more | 152 |
| abstract_inverted_index.most | 6 |
| abstract_inverted_index.size | 98 |
| abstract_inverted_index.task | 131, 151 |
| abstract_inverted_index.that | 122 |
| abstract_inverted_index.time | 185 |
| abstract_inverted_index.with | 14, 134 |
| abstract_inverted_index.(FOG) | 3 |
| abstract_inverted_index.(PD), | 17 |
| abstract_inverted_index.1023, | 161 |
| abstract_inverted_index.based | 36 |
| abstract_inverted_index.best. | 180 |
| abstract_inverted_index.model | 74 |
| abstract_inverted_index.paper | 30 |
| abstract_inverted_index.reach | 178 |
| abstract_inverted_index.solve | 92 |
| abstract_inverted_index.speed | 66 |
| abstract_inverted_index.well. | 132 |
| abstract_inverted_index.which | 18 |
| abstract_inverted_index.(VMD). | 42 |
| abstract_inverted_index.87.5%. | 198 |
| abstract_inverted_index.87.8%, | 189 |
| abstract_inverted_index.89.7%, | 193 |
| abstract_inverted_index.common | 7 |
| abstract_inverted_index.method | 35, 51 |
| abstract_inverted_index.number | 157, 164 |
| abstract_inverted_index.proves | 121 |
| abstract_inverted_index.sample | 97 |
| abstract_inverted_index.single | 104 |
| abstract_inverted_index.social | 25 |
| abstract_inverted_index.splits | 159 |
| abstract_inverted_index.Disease | 16 |
| abstract_inverted_index.affects | 20 |
| abstract_inverted_index.feature | 82 |
| abstract_inverted_index.improve | 62 |
| abstract_inverted_index.instead | 45 |
| abstract_inverted_index.maximum | 156 |
| abstract_inverted_index.perform | 80 |
| abstract_inverted_index.problem | 94 |
| abstract_inverted_index.quality | 23 |
| abstract_inverted_index.signal. | 59 |
| abstract_inverted_index.Bayesian | 116 |
| abstract_inverted_index.Compared | 133 |
| abstract_inverted_index.Finally, | 106 |
| abstract_inverted_index.Firstly, | 43 |
| abstract_inverted_index.Freezing | 0 |
| abstract_inverted_index.RUSBoost | 88, 124, 143, 174 |
| abstract_inverted_index.accuracy | 64, 182 |
| abstract_inverted_index.adaptive | 54 |
| abstract_inverted_index.analysis | 50 |
| abstract_inverted_index.complete | 53, 127, 147 |
| abstract_inverted_index.disorder | 11 |
| abstract_inverted_index.ensemble | 89, 111, 140, 144, 175 |
| abstract_inverted_index.function | 26 |
| abstract_inverted_index.patients | 13 |
| abstract_inverted_index.proposes | 31 |
| abstract_inverted_index.Adaboost, | 136 |
| abstract_inverted_index.Secondly, | 60 |
| abstract_inverted_index.algorithm | 90, 125, 145, 176, 187 |
| abstract_inverted_index.dimension | 83 |
| abstract_inverted_index.disabling | 9 |
| abstract_inverted_index.optimized | 114 |
| abstract_inverted_index.patients. | 28 |
| abstract_inverted_index.seriously | 19 |
| abstract_inverted_index.algorithm, | 70 |
| abstract_inverted_index.classifier | 78, 112 |
| abstract_inverted_index.experiment | 120 |
| abstract_inverted_index.reduction. | 84 |
| abstract_inverted_index.unbalanced | 96 |
| abstract_inverted_index.Variational | 39 |
| abstract_inverted_index.algorithms, | 141 |
| abstract_inverted_index.classifier. | 105 |
| abstract_inverted_index.classifiers | 167 |
| abstract_inverted_index.limitations | 101 |
| abstract_inverted_index.performance | 171 |
| abstract_inverted_index.recognition | 34, 69, 130, 150, 186 |
| abstract_inverted_index.sensitivity | 191 |
| abstract_inverted_index.specificity | 196 |
| abstract_inverted_index.traditional | 48 |
| abstract_inverted_index.ROS-Adaboost | 139 |
| abstract_inverted_index.considerable | 100 |
| abstract_inverted_index.efficiently. | 153 |
| abstract_inverted_index.Decomposition | 41 |
| abstract_inverted_index.Parkinson’s | 15 |
| abstract_inverted_index.decomposition | 55 |
| abstract_inverted_index.optimization, | 117 |
| abstract_inverted_index.time-frequency | 49 |
| abstract_inverted_index.hyperparam-eters | 108 |
| abstract_inverted_index.Tomeklinks-Adaboost | 137 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile.value | 0.13610463 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |