Document Zbl 07814681

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Sci.</a> <a href="/?q=in%3A509612" title="Articles in this Issue">605, 225-243 (2022)</a>. </div> <div class="abstract">Summary: The application of multisource information fusion in real-world scenarios is an emerging practice because it effectively uses consistent and complementary data to optimize decision-making. Dempster-Shafer (D-S) evidence theory is prevalent because it competently handles uncertainty problems by assigning basic probability assignments (BPAs) to multielement subsets. However, a counterintuitive result may be obtained when the evidence is highly conflicting. To overcome this flaw, this paper defines a new divergence measurement to quantify the differences between BPAs; we name this new metric the belief Rényi divergence. The belief Rényi divergence takes the number of possible hypotheses into consideration, which makes it a more rational and effective difference measurement in the realm of evidence theory. Additionally, some important properties of the belief Rényi divergence are extensively explored and proven, in which the belief Rényi divergence also connects to Kullback-Leibler divergence, Hellinger distance and $\chi^2$ divergence. Moreover, a novel multisource information fusion method is devised based on the proposed belief Rényi divergence and belief entropy. Our proposed belief Rényi divergence can efficiently model the differences between evidence, and the belief entropy is used to calculate the information volume of evidence. Thus, the proposed method can sufficiently exploit the relationships among evidence and the information volume of the evidence itself. Two case studies are illustrated to verify the effectiveness and practicality of the proposed method. Also, an experiment on an iris dataset classification is presented to verify the performance of the proposed method. In addition, an EEG data analysis application demonstrates that the proposed method can be effectively used in real-world applications.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A7814681">Cited in <strong>13</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A68T09" title="MSC2020">68T09</a> </td> <td class="space"> Computational aspects of data analysis and big data </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A68T37" title="MSC2020">68T37</a> </td> <td class="space"> Reasoning under uncertainty in the context of artificial intelligence </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A92C55" title="MSC2020">92C55</a> </td> <td class="space"> Biomedical imaging and signal processing </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A94A17" title="MSC2020">94A17</a> </td> <td class="space"> Measures of information, entropy </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Ainformation+fusion">information fusion</a>; <a href="/?q=ut%3ADempster-Shafer+evidence+theory">Dempster-Shafer evidence theory</a>; <a href="/?q=ut%3Abelief+divergence+measure">belief divergence measure</a>; <a href="/?q=ut%3AR%C3%A9nyi+divergence">Rényi divergence</a>; <a href="/?q=ut%3Abelief+R%C3%A9nyi+divergence">belief Rényi divergence</a>; <a href="/?q=ut%3Abelief+entropy">belief entropy</a>; <a href="/?q=ut%3Aelectroencephalography+%28EEG%29">electroencephalography (EEG)</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;">  </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function">  <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 07814681" data-ciurl="/ci/07814681" data-biburl="/bibtex/07814681.bib" data-amsurl="/amsrefs/07814681.bib" data-xmlurl="/xml/07814681.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/07814681.pdf" title="Zbl 07814681 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1016/j.ins.2022.05.012" aria-label="DOI for “A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis”" title="10.1016/j.ins.2022.05.012">DOI</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Lai, J. 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