Fractal Belief Rényi Divergence With its Applications in Pattern Classification
Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information effectively. Pattern classification is the core research content of pattern recognition, and multisource information fusion based on D-S evidence th...
Saved in:
| Published in | IEEE transactions on knowledge and data engineering Vol. 36; no. 12; pp. 8297 - 8312 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.12.2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1041-4347 1558-2191 |
| DOI | 10.1109/TKDE.2023.3342907 |
Cover
Loading…
| Abstract | Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information effectively. Pattern classification is the core research content of pattern recognition, and multisource information fusion based on D-S evidence theory can be effectively applied to pattern classification problems. However, in D-S evidence theory, highly-conflicting evidence may cause counterintuitive fusion results. Belief divergence theory is one of the theories that are proposed to address problems of highly-conflicting evidence. Although belief divergence can deal with conflict between evidence, none of the existing belief divergence methods has considered how to effectively measure the discrepancy between two pieces of evidence with time evolutionary. In this study, a novel fractal belief Rényi (FBR) divergence is proposed to handle this problem. We assume that it is the first divergence that extends the concept of fractal to Rényi divergence. The advantage is measuring the discrepancy between two pieces of evidence with time evolution, which satisfies several properties and is flexible and practical in various circumstances. Furthermore, a novel algorithm for multisource information fusion based on FBR divergence, namely FBReD-based weighted multisource information fusion, is developed. Ultimately, the proposed multisource information fusion algorithm is applied to a series of experiments for pattern classification based on real datasets, where our proposed algorithm achieved superior performance. |
|---|---|
| AbstractList | Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information effectively. Pattern classification is the core research content of pattern recognition, and multisource information fusion based on D-S evidence theory can be effectively applied to pattern classification problems. However, in D-S evidence theory, highly-conflicting evidence may cause counterintuitive fusion results. Belief divergence theory is one of the theories that are proposed to address problems of highly-conflicting evidence. Although belief divergence can deal with conflict between evidence, none of the existing belief divergence methods has considered how to effectively measure the discrepancy between two pieces of evidence with time evolutionary. In this study, a novel fractal belief Rényi (FBR) divergence is proposed to handle this problem. We assume that it is the first divergence that extends the concept of fractal to Rényi divergence. The advantage is measuring the discrepancy between two pieces of evidence with time evolution, which satisfies several properties and is flexible and practical in various circumstances. Furthermore, a novel algorithm for multisource information fusion based on FBR divergence, namely FBReD-based weighted multisource information fusion, is developed. Ultimately, the proposed multisource information fusion algorithm is applied to a series of experiments for pattern classification based on real datasets, where our proposed algorithm achieved superior performance. |
| Author | Huang, Yingcheng Cao, Zehong Xiao, Fuyuan Lin, Chin-Teng |
| Author_xml | – sequence: 1 givenname: Yingcheng orcidid: 0009-0003-1713-7708 surname: Huang fullname: Huang, Yingcheng email: huangyingchengcqu@163.com organization: School of Big Data and Software Engineering, Chongqing University, Chongqing, China – sequence: 2 givenname: Fuyuan orcidid: 0000-0002-6303-895X surname: Xiao fullname: Xiao, Fuyuan email: doctorxiaofy@hotmail.com organization: School of Big Data and Software Engineering, Chongqing University, Chongqing, China – sequence: 3 givenname: Zehong orcidid: 0000-0003-3656-0328 surname: Cao fullname: Cao, Zehong email: zhcaonctu@gmail.com organization: STEM, University of South Australia, Adelaide, SA, Australia – sequence: 4 givenname: Chin-Teng orcidid: 0000-0001-8371-8197 surname: Lin fullname: Lin, Chin-Teng email: chin-teng.lin@uts.edu.au organization: Australia AI Institute, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia |
| BookMark | eNp9kM9KAzEQh4NUsK0-gOAhL7BrZrPZZI-1f1QsWKTicYnTRCNrWpIg9JF8Dl_Mre1BPMgcZmD4Zn58A9Lza28IOQeWA7D6cnk3meYFK3jOeVnUTB6RPgihsgJq6HUzKyEreSlPyCDGN8aYkgr6ZDELGpNu6ZVpnbH04evTbx2duA8TXoxHQ59ceqUuRTrabFqHOrm1j9R5utApmeDpuNUxOntYnZJjq9tozg59SB5n0-X4JpvfX9-OR_MMi6pKGUCXDkFo-wyFqDmYlWYAWjBkiGh4vZKyUkygtRJLVckSdSVQQWWFKjUfEtjfxbCOMRjbbIJ712HbAGt2SpqdkmanpDko6Rj5h0GXflKnoF37L3mxJ50x5tcnXnUF_BsDeHF6 |
| CODEN | ITKEEH |
| CitedBy_id | crossref_primary_10_1007_s40747_024_01569_y crossref_primary_10_1063_5_0220154 crossref_primary_10_1016_j_ins_2024_120617 crossref_primary_10_1016_j_patcog_2024_110720 crossref_primary_10_1016_j_cja_2024_103350 crossref_primary_10_1016_j_fss_2024_109034 crossref_primary_10_3390_e27010094 crossref_primary_10_1007_s40314_024_02781_9 crossref_primary_10_1016_j_engappai_2024_109056 crossref_primary_10_1080_03610926_2024_2301986 crossref_primary_10_1016_j_engappai_2024_108164 crossref_primary_10_1016_j_chaos_2024_115488 crossref_primary_10_1016_j_ins_2024_120168 crossref_primary_10_1016_j_engappai_2025_110412 crossref_primary_10_1109_TKDE_2025_3530524 |
| Cites_doi | 10.3846/mma.2022.14060 10.1109/tcyb.2021.3063285 10.1109/TKDE.2020.2997043 10.1016/j.engappai.2022.105701 10.1002/bies.202000178 10.1109/TKDE.2022.3177896 10.1016/j.knosys.2022.109680 10.1109/TKDE.2022.3206871 10.24963/ijcai.2022/28 10.1016/j.ins.2019.12.037 10.1016/j.ins.2023.01.105 10.15837/ijccc.2020.6.3983 10.1109/TFUZZ.2019.2955047 10.1109/TSMC.2016.2628879 10.1109/TPAMI.2022.3227913 10.1109/TKDE.2020.3048788 10.1109/tfuzz.2020.3007423 10.1109/tgrs.2023.3244565 10.1038/s41597-019-0027-4 10.1214/aoms/1177698950 10.15837/ijccc.2022.1.4542 10.1109/TPAMI.2022.3167045 10.1007/s11432-020-3006-9 10.1109/TFUZZ.2017.2788881 10.1016/j.ejor.2020.12.011 10.1109/TKDE.2021.3049540 10.1109/TSMC.2022.3180174 10.1109/TKDE.2020.3046645 10.1108/IJSI-05-2022-0076 10.1007/s00500-021-06658-5 10.1109/TKDE.2020.2973981 10.1016/j.net.2021.09.045 10.1109/TKDE.2020.2991000 10.1109/TFUZZ.2020.3003501 10.1016/j.cie.2022.108818 10.1016/j.ins.2021.08.083 10.1016/j.ins.2023.119177 10.1109/TIP.2023.3243521 10.1016/j.inffus.2023.01.026 10.1016/j.ins.2021.08.088 10.1142/S0218348X22501109 10.1109/TKDE.2020.3015914 10.1109/TFUZZ.2017.2709275 10.1109/TKDE.2022.3193569 10.24963/ijcai.2022/76 10.1007/s10489-022-04181-0 10.1109/TIT.2021.3085190 10.1016/j.ins.2019.11.022 10.32604/cmes.2022.018123 10.3390/math10132325 10.1109/TKDE.2020.3039469 10.1109/TFUZZ.2018.2859899 10.1109/TSMC.2022.3205365 10.1016/j.asoc.2019.105703 10.1109/TSMC.2022.3211498 10.1007/s00500-022-07351-x 10.1109/TSMC.2019.2944640 10.1109/TCYB.2017.2710205 10.1016/j.engappai.2022.105362 10.1016/j.ins.2022.05.012 10.1016/j.ins.2023.119061 10.1109/TIT.2022.3209892 10.1515/9780691214696 10.1109/TAC.2021.3054064 10.1109/TKDE.2020.3015959 10.1016/j.ijar.2020.02.002 10.1109/TCYB.2021.3052536 10.1145/3485447.3512184 10.1002/int.22912 10.1109/TIT.2014.2320500 10.1016/j.ins.2023.119189 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION |
| DOI | 10.1109/TKDE.2023.3342907 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1558-2191 |
| EndPage | 8312 |
| ExternalDocumentID | 10_1109_TKDE_2023_3342907 10363631 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Australian Research Council grantid: DE220100265 funderid: 10.13039/501100000923 – fundername: ARC Discovery grantid: DP220100803 – fundername: Fundamental Research Funds for the Central Universities grantid: 2023CDJXY-035 funderid: 10.13039/501100012226 – fundername: Natural Science Foundation of Chongqing, China grantid: CSTB2022NSCQ-MSX0531 – fundername: Chongqing Overseas Scholars Innovation Program grantid: cx2022024 – fundername: National Natural Science Foundation of China grantid: 62003280 funderid: 10.13039/501100001809 – fundername: Chongqing Talents: Exceptional Young Talents grantid: cstc2022ycjh-bgzxm0070 |
| GroupedDBID | -~X .DC 0R~ 1OL 29I 4.4 5GY 5VS 6IK 97E 9M8 AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ H~9 ICLAB IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNI RNS RXW RZB TAE TAF TN5 UHB VH1 AAYOK AAYXX CITATION RIG |
| ID | FETCH-LOGICAL-c266t-11155c15afb125931eda011a50c0ccce39d776805cff7c48674ca65c816f584a3 |
| IEDL.DBID | RIE |
| ISSN | 1041-4347 |
| IngestDate | Tue Jul 01 01:19:44 EDT 2025 Thu Apr 24 23:06:25 EDT 2025 Wed Aug 27 03:06:49 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c266t-11155c15afb125931eda011a50c0ccce39d776805cff7c48674ca65c816f584a3 |
| ORCID | 0009-0003-1713-7708 0000-0001-8371-8197 0000-0002-6303-895X 0000-0003-3656-0328 |
| PageCount | 16 |
| ParticipantIDs | crossref_primary_10_1109_TKDE_2023_3342907 crossref_citationtrail_10_1109_TKDE_2023_3342907 ieee_primary_10363631 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-12-01 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE transactions on knowledge and data engineering |
| PublicationTitleAbbrev | TKDE |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref71 ref70 ref24 ref68 ref23 ref67 ref26 Wang (ref72) ref25 ref69 ref20 ref64 ref63 ref22 ref66 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61 |
| References_xml | – ident: ref40 doi: 10.3846/mma.2022.14060 – ident: ref56 doi: 10.1109/tcyb.2021.3063285 – ident: ref20 doi: 10.1109/TKDE.2020.2997043 – ident: ref49 doi: 10.1016/j.engappai.2022.105701 – ident: ref16 doi: 10.1002/bies.202000178 – ident: ref47 doi: 10.1109/TKDE.2022.3177896 – ident: ref53 doi: 10.1016/j.knosys.2022.109680 – ident: ref34 doi: 10.1109/TKDE.2022.3206871 – ident: ref28 doi: 10.24963/ijcai.2022/28 – ident: ref54 doi: 10.1016/j.ins.2019.12.037 – ident: ref4 doi: 10.1016/j.ins.2023.01.105 – ident: ref62 doi: 10.15837/ijccc.2020.6.3983 – ident: ref24 doi: 10.1109/TFUZZ.2019.2955047 – ident: ref35 doi: 10.1109/TSMC.2016.2628879 – ident: ref55 doi: 10.1109/TPAMI.2022.3227913 – ident: ref67 doi: 10.1109/TKDE.2020.3048788 – ident: ref19 doi: 10.1109/tfuzz.2020.3007423 – ident: ref29 doi: 10.1109/tgrs.2023.3244565 – ident: ref25 doi: 10.1038/s41597-019-0027-4 – ident: ref50 doi: 10.1214/aoms/1177698950 – ident: ref3 doi: 10.15837/ijccc.2022.1.4542 – ident: ref8 doi: 10.1109/TPAMI.2022.3167045 – ident: ref14 doi: 10.1007/s11432-020-3006-9 – ident: ref22 doi: 10.1109/TFUZZ.2017.2788881 – ident: ref57 doi: 10.1016/j.ejor.2020.12.011 – ident: ref65 doi: 10.1109/TKDE.2021.3049540 – ident: ref13 doi: 10.1109/TSMC.2022.3180174 – ident: ref66 doi: 10.1109/TKDE.2020.3046645 – ident: ref26 doi: 10.1108/IJSI-05-2022-0076 – start-page: 20536 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref72 article-title: MVP-N: A dataset and benchmark for real-world multi-view object classification – ident: ref5 doi: 10.1007/s00500-021-06658-5 – ident: ref38 doi: 10.1109/TKDE.2020.2973981 – ident: ref21 doi: 10.1016/j.net.2021.09.045 – ident: ref70 doi: 10.1109/TKDE.2020.2991000 – ident: ref6 doi: 10.1109/TFUZZ.2020.3003501 – ident: ref17 doi: 10.1016/j.cie.2022.108818 – ident: ref31 doi: 10.1016/j.ins.2021.08.083 – ident: ref9 doi: 10.1016/j.ins.2023.119177 – ident: ref2 doi: 10.1109/TIP.2023.3243521 – ident: ref18 doi: 10.1016/j.inffus.2023.01.026 – ident: ref36 doi: 10.1016/j.ins.2021.08.088 – ident: ref63 doi: 10.1142/S0218348X22501109 – ident: ref69 doi: 10.1109/TKDE.2020.3015914 – ident: ref15 doi: 10.1109/TFUZZ.2017.2709275 – ident: ref1 doi: 10.1109/TKDE.2022.3193569 – ident: ref27 doi: 10.24963/ijcai.2022/76 – ident: ref10 doi: 10.1007/s10489-022-04181-0 – ident: ref43 doi: 10.1109/TIT.2021.3085190 – ident: ref44 doi: 10.1016/j.ins.2019.11.022 – ident: ref23 doi: 10.32604/cmes.2022.018123 – ident: ref52 doi: 10.3390/math10132325 – ident: ref68 doi: 10.1109/TKDE.2020.3039469 – ident: ref60 doi: 10.1109/TFUZZ.2018.2859899 – ident: ref32 doi: 10.1109/TSMC.2022.3205365 – ident: ref7 doi: 10.1016/j.asoc.2019.105703 – ident: ref48 doi: 10.1109/TSMC.2022.3211498 – ident: ref64 doi: 10.1007/s00500-022-07351-x – ident: ref12 doi: 10.1109/TSMC.2019.2944640 – ident: ref71 doi: 10.1109/TCYB.2017.2710205 – ident: ref37 doi: 10.1016/j.engappai.2022.105362 – ident: ref41 doi: 10.1016/j.ins.2022.05.012 – ident: ref58 doi: 10.1016/j.ins.2023.119061 – ident: ref42 doi: 10.1109/TIT.2022.3209892 – ident: ref51 doi: 10.1515/9780691214696 – ident: ref11 doi: 10.1109/TAC.2021.3054064 – ident: ref39 doi: 10.1109/TKDE.2020.3015959 – ident: ref59 doi: 10.1016/j.ijar.2020.02.002 – ident: ref33 doi: 10.1109/TCYB.2021.3052536 – ident: ref30 doi: 10.1145/3485447.3512184 – ident: ref45 doi: 10.1002/int.22912 – ident: ref61 doi: 10.1109/TIT.2014.2320500 – ident: ref46 doi: 10.1016/j.ins.2023.119189 |
| SSID | ssj0008781 |
| Score | 2.5385373 |
| Snippet | Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information... |
| SourceID | crossref ieee |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 8297 |
| SubjectTerms | Australia Dempster-Shafer evidence theory Diseases Evidence theory fractal Fractals Medical diagnostic imaging Medical services multisource information fusion pattern classification Rényi divergence Time measurement |
| Title | Fractal Belief Rényi Divergence With its Applications in Pattern Classification |
| URI | https://ieeexplore.ieee.org/document/10363631 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8NAEF60Jz1YrRXriz14EhI33Wwex2pbimIp0mJvIZndYFBS0fSg_8jf4R9zdpOWICiSS14LS2Z3Z77szPcRci5B-rzLXMuVAix0AYGVpF1heYkHMkQYFIe6OPlu7I1m7s1czKtidVMLo5QyyWfK1qdmL18uYKl_leEM5x4eCHY2EbmVxVrrZTfwjSIpwgsERdz1qy1Mh4WX09v-wNY64TbnuP5q6diaE6qpqhinMmyS8ao7ZS7Jk70sEhs-fjA1_ru_u2SnCi9prxwPe2RD5S3SXEk30Gomt8h2jYdwn0yGulYK210pDElTev_1mb9ntK9zNgxZJ33IikeaFW-0V9vwpllOJ4afM6dGXFOnHZlHbTIbDqbXI6uSWrAAPXRh4YonBDgiThOMeELuKBnjzI8FAwYAiofSR2DCBKSpD5qlz4XYExA4XoohTMwPSCNf5OqQUKYArz1HORKjHTcOBIvDxAUmpINwLekQtvr2EVQ85FoO4zkyeISFkTZXpM0VVebqkIt1k5eShOOvl9vaErUXSyMc_XL_mGxhc7dMUTkhjeJ1qU4x0CiSMzPAvgEFf830 |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3JTsMwELVQOQAHllLEjg-ckBIcbCfNsVCqslUVakVvUTJ2RAQKCNID_BHfwY8xdlIUIYFQLlnsyMrYnpnMzHuEHCpQAT9hwhFKgoMqoO0k6Yl0_MQHFaIbFIemOPlm4PfH4nIiJ1Wxuq2F0Vrb5DPtmlMby1dPMDW_ynCFcx8PdHbmUfGLsCzX-t5424HlJEUHA90iLoIqiOmx8Hh01T13DVO4yznuwIY8tqaGarwqVq30VshgNqAym-TBnRaJC-8_sBr_PeJVslwZmLRTzog1MqfzJlmZkTfQai03yVINiXCdDHumWgr7nWo0SlN6-_mRv2W0a7I2LFwnvcuKe5oVr7RTC3nTLKdDi9CZU0uvaRKP7KMWGffOR2d9pyJbcAB1dOHgnicleDJOE7R5Qu5pFePajyUDBgCahypA14RJSNMADE6fgNiX0Pb8FI2YmG-QRv6U601CmQa89j3tKbR3RNyWLA4TAUwqDx22ZIuw2bePoEIiN4QYj5H1SFgYGXFFRlxRJa4tcvTd5bmE4fircctIotawFML2L_cPyEJ_dHMdXV8MrnbIIr5KlAkru6RRvEz1HpodRbJvJ9sXAorRRA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fractal+Belief+R%C3%A9nyi+Divergence+With+its+Applications+in+Pattern+Classification&rft.jtitle=IEEE+transactions+on+knowledge+and+data+engineering&rft.au=Huang%2C+Yingcheng&rft.au=Xiao%2C+Fuyuan&rft.au=Cao%2C+Zehong&rft.au=Lin%2C+Chin-Teng&rft.date=2024-12-01&rft.pub=IEEE&rft.issn=1041-4347&rft.volume=36&rft.issue=12&rft.spage=8297&rft.epage=8312&rft_id=info:doi/10.1109%2FTKDE.2023.3342907&rft.externalDocID=10363631 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1041-4347&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1041-4347&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1041-4347&client=summon |