Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition
The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especial...
Gespeichert in:
Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2024-10, Vol.35 (10), p.13589-13603 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 13603 |
---|---|
container_issue | 10 |
container_start_page | 13589 |
container_title | IEEE transaction on neural networks and learning systems |
container_volume | 35 |
creator | Dong, Yilin Li, Xinde Dezert, Jean Zhou, Rigui Zuo, Kezhu Ge, Shuzhi Sam |
description | The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods. |
doi_str_mv | 10.1109/TNNLS.2023.3270290 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_10132427</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10132427</ieee_id><sourcerecordid>2819278307</sourcerecordid><originalsourceid>FETCH-LOGICAL-c309t-e9c33e600b24af68fcabc5c587c3405be573f397e3477056e74528d676cc89de3</originalsourceid><addsrcrecordid>eNpNkF1LwzAUhoMoTtQ_ICK91IvO5KRt0ss5nBPmB26CdyHLTmeka2fSDPbv7dwc5ibhnOd9CQ8hF4x2GaP57eT5eTTuAgXe5SAo5PSAnADLIAYu5eH-LT465Nz7L9qejKZZkh-TDhcACU_hhLw-OL38jMeNC6YJDuM77XEWPYWysXOnq1BqF91habGIBsHbuoqK2kXDsNBV1DONXdlmHb2hqeeVbdr1GTkqdOnxfHefkvfB_aQ_jEcvD4_93ig2nOZNjLnhHDNKp5DoIpOF0VOTmlQKwxOaTjEVvOC5QJ4I0X4bRZKCnGUiM0bmM-Sn5Gbb-6lLtXR2od1a1dqqYW-kNjOaZJBKyVasZa-37NLV3wF9oxbWGyxLXWEdvALJchCSU9GisEWNq713WOy7GVUb8epXvNqIVzvxbehq1x-mC5ztI3-aW-ByC1hE_NfIOCQg-A_YF4ZA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2819278307</pqid></control><display><type>article</type><title>Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition</title><source>IEEE Electronic Library (IEL)</source><creator>Dong, Yilin ; Li, Xinde ; Dezert, Jean ; Zhou, Rigui ; Zuo, Kezhu ; Ge, Shuzhi Sam</creator><creatorcontrib>Dong, Yilin ; Li, Xinde ; Dezert, Jean ; Zhou, Rigui ; Zuo, Kezhu ; Ge, Shuzhi Sam</creatorcontrib><description>The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.</description><identifier>ISSN: 2162-237X</identifier><identifier>ISSN: 2162-2388</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2023.3270290</identifier><identifier>PMID: 37224352</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Approximation methods ; Basic belief assignment (BBA) ; belief functions (BFs) ; Computational complexity ; Decision making ; Engineering Sciences ; Evidence theory ; graph networks ; Human Activities ; human activity recognition (HAR) ; Humans ; Learning systems ; Measurement ; multigranular fusion ; Neural Networks, Computer ; Pattern Recognition, Automated - methods ; Physics ; Visualization</subject><ispartof>IEEE transaction on neural networks and learning systems, 2024-10, Vol.35 (10), p.13589-13603</ispartof><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c309t-e9c33e600b24af68fcabc5c587c3405be573f397e3477056e74528d676cc89de3</cites><orcidid>0000-0001-5549-312X ; 0000-0002-8894-8108 ; 0000-0002-4441-3355 ; 0000-0002-1529-4537 ; 0000-0003-3474-9186 ; 0000-0002-9426-1599</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10132427$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,315,781,785,797,886,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10132427$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37224352$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04625881$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Dong, Yilin</creatorcontrib><creatorcontrib>Li, Xinde</creatorcontrib><creatorcontrib>Dezert, Jean</creatorcontrib><creatorcontrib>Zhou, Rigui</creatorcontrib><creatorcontrib>Zuo, Kezhu</creatorcontrib><creatorcontrib>Ge, Shuzhi Sam</creatorcontrib><title>Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.</description><subject>Algorithms</subject><subject>Approximation methods</subject><subject>Basic belief assignment (BBA)</subject><subject>belief functions (BFs)</subject><subject>Computational complexity</subject><subject>Decision making</subject><subject>Engineering Sciences</subject><subject>Evidence theory</subject><subject>graph networks</subject><subject>Human Activities</subject><subject>human activity recognition (HAR)</subject><subject>Humans</subject><subject>Learning systems</subject><subject>Measurement</subject><subject>multigranular fusion</subject><subject>Neural Networks, Computer</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Physics</subject><subject>Visualization</subject><issn>2162-237X</issn><issn>2162-2388</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpNkF1LwzAUhoMoTtQ_ICK91IvO5KRt0ss5nBPmB26CdyHLTmeka2fSDPbv7dwc5ibhnOd9CQ8hF4x2GaP57eT5eTTuAgXe5SAo5PSAnADLIAYu5eH-LT465Nz7L9qejKZZkh-TDhcACU_hhLw-OL38jMeNC6YJDuM77XEWPYWysXOnq1BqF91habGIBsHbuoqK2kXDsNBV1DONXdlmHb2hqeeVbdr1GTkqdOnxfHefkvfB_aQ_jEcvD4_93ig2nOZNjLnhHDNKp5DoIpOF0VOTmlQKwxOaTjEVvOC5QJ4I0X4bRZKCnGUiM0bmM-Sn5Gbb-6lLtXR2od1a1dqqYW-kNjOaZJBKyVasZa-37NLV3wF9oxbWGyxLXWEdvALJchCSU9GisEWNq713WOy7GVUb8epXvNqIVzvxbehq1x-mC5ztI3-aW-ByC1hE_NfIOCQg-A_YF4ZA</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Dong, Yilin</creator><creator>Li, Xinde</creator><creator>Dezert, Jean</creator><creator>Zhou, Rigui</creator><creator>Zuo, Kezhu</creator><creator>Ge, Shuzhi Sam</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-5549-312X</orcidid><orcidid>https://orcid.org/0000-0002-8894-8108</orcidid><orcidid>https://orcid.org/0000-0002-4441-3355</orcidid><orcidid>https://orcid.org/0000-0002-1529-4537</orcidid><orcidid>https://orcid.org/0000-0003-3474-9186</orcidid><orcidid>https://orcid.org/0000-0002-9426-1599</orcidid></search><sort><creationdate>20241001</creationdate><title>Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition</title><author>Dong, Yilin ; Li, Xinde ; Dezert, Jean ; Zhou, Rigui ; Zuo, Kezhu ; Ge, Shuzhi Sam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c309t-e9c33e600b24af68fcabc5c587c3405be573f397e3477056e74528d676cc89de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Approximation methods</topic><topic>Basic belief assignment (BBA)</topic><topic>belief functions (BFs)</topic><topic>Computational complexity</topic><topic>Decision making</topic><topic>Engineering Sciences</topic><topic>Evidence theory</topic><topic>graph networks</topic><topic>Human Activities</topic><topic>human activity recognition (HAR)</topic><topic>Humans</topic><topic>Learning systems</topic><topic>Measurement</topic><topic>multigranular fusion</topic><topic>Neural Networks, Computer</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Physics</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Dong, Yilin</creatorcontrib><creatorcontrib>Li, Xinde</creatorcontrib><creatorcontrib>Dezert, Jean</creatorcontrib><creatorcontrib>Zhou, Rigui</creatorcontrib><creatorcontrib>Zuo, Kezhu</creatorcontrib><creatorcontrib>Ge, Shuzhi Sam</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dong, Yilin</au><au>Li, Xinde</au><au>Dezert, Jean</au><au>Zhou, Rigui</au><au>Zuo, Kezhu</au><au>Ge, Shuzhi Sam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2024-10-01</date><risdate>2024</risdate><volume>35</volume><issue>10</issue><spage>13589</spage><epage>13603</epage><pages>13589-13603</pages><issn>2162-237X</issn><issn>2162-2388</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>37224352</pmid><doi>10.1109/TNNLS.2023.3270290</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-5549-312X</orcidid><orcidid>https://orcid.org/0000-0002-8894-8108</orcidid><orcidid>https://orcid.org/0000-0002-4441-3355</orcidid><orcidid>https://orcid.org/0000-0002-1529-4537</orcidid><orcidid>https://orcid.org/0000-0003-3474-9186</orcidid><orcidid>https://orcid.org/0000-0002-9426-1599</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 2162-237X |
ispartof | IEEE transaction on neural networks and learning systems, 2024-10, Vol.35 (10), p.13589-13603 |
issn | 2162-237X 2162-2388 2162-2388 |
language | eng |
recordid | cdi_ieee_primary_10132427 |
source | IEEE Electronic Library (IEL) |
subjects | Algorithms Approximation methods Basic belief assignment (BBA) belief functions (BFs) Computational complexity Decision making Engineering Sciences Evidence theory graph networks Human Activities human activity recognition (HAR) Humans Learning systems Measurement multigranular fusion Neural Networks, Computer Pattern Recognition, Automated - methods Physics Visualization |
title | Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T15%3A36%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Graph-Structure-Based%20Multigranular%20Belief%20Fusion%20for%20Human%20Activity%20Recognition&rft.jtitle=IEEE%20transaction%20on%20neural%20networks%20and%20learning%20systems&rft.au=Dong,%20Yilin&rft.date=2024-10-01&rft.volume=35&rft.issue=10&rft.spage=13589&rft.epage=13603&rft.pages=13589-13603&rft.issn=2162-237X&rft.eissn=2162-2388&rft.coden=ITNNAL&rft_id=info:doi/10.1109/TNNLS.2023.3270290&rft_dat=%3Cproquest_RIE%3E2819278307%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2819278307&rft_id=info:pmid/37224352&rft_ieee_id=10132427&rfr_iscdi=true |