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A complex weighted discounting multisource information fusion with Its application in pattern classification

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dc.contributor.authorXiao, F.
dc.contributor.authorCao, Z.
dc.contributor.authorLin, C.T.
dc.date.issued2023
dc.description.abstractComplex evidence theory (CET) is an effective method for uncertainty reasoning in knowledge-based systems with good interpretability that has recently attracted much attention. However, approaches to improve the performance of uncertainty reasoning in CET-based expert systems remains an open issue. One key to performance improvement is the adequate management of conflict from multisource information. In this paper, a generalized correlation coefficient, namely, the complex evidential correlation coefficient (CECC), is proposed for the complex mass functions or complex basic belief assignments (CBBAs) in CET. On this basis, a complex conflict coefficient is proposed to measure the conflict between CBBAs; when CBBAs turn into classic BBAs, the complex correlation and conflict coefficients will degrade into traditional coefficients. The complex conflict coefficient satisfies nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement properties, which are desirable for conflict measurement. Several numerical examples validate through comparisons the superiority of the complex conflict coefficient. In this context, a weighted discounting multisource information fusion algorithm, which is called the CECC-WDMSIF, is designed based on the CECC to improve the performance of CET-based expert systems. By applying the CECC-WDMSIF method to the pattern classification of diverse real-world datasets, it is demonstrated that the proposed CECC-WDMSIF outperforms well-known related approaches with higher classification accuracy and robustness.
dc.identifier.citationIEEE Transactions on Knowledge and Data Engineering, 2023; 35(8):7609-7623
dc.identifier.doi10.1109/TKDE.2022.3206871
dc.identifier.issn1041-4347
dc.identifier.issn1558-2191
dc.identifier.orcidCao, Z. [0000-0003-3656-0328]
dc.identifier.urihttps://hdl.handle.net/11541.2/30733
dc.language.isoen
dc.publisherIEEE
dc.relation.fundingARC DE220100265 DECRA Fellowship
dc.relation.fundingNational Natural Science Foundation of China 62003280
dc.relation.fundingChongqing Talents: Exceptional Young Talents Project cstc2022ycjh-bgzxm0070
dc.rightsCopyright 2022 IEEE Access Condition Notes: Accepted manuscript is available open access
dc.source.urihttps://doi.org/10.1109/TKDE.2022.3206871
dc.subjectcomplex evidence theory
dc.subjectcomplex evidential correlation coefficient
dc.subjectcomplex mass function
dc.subjectconflict management
dc.subjectexpert system
dc.subjectpattern classification
dc.subjectuncertainty reasoning
dc.titleA complex weighted discounting multisource information fusion with Its application in pattern classification
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12251109230001831 13251109220001831 Open Access Postprint
ror.mmsid9916681927201831

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