<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-02118829</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-20T23:28:14+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">Semantic Channel and Shannon’s Channel Mutually Match for Multi-label Classification</title>
            <author role="aut">
              <persName>
                <forename type="first">Chenguang</forename>
                <surname>Lu</surname>
              </persName>
              <email type="md5">6468fcb92f13ddd3293c5bd281dfe3de</email>
              <email type="domain">foxmail.com</email>
              <idno type="idhal" notation="numeric">1033431</idno>
              <idno type="halauthorid" notation="string">1385620-1033431</idno>
              <affiliation ref="#struct-557661"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Hal</forename>
                <surname>Ifip</surname>
              </persName>
              <email type="md5">2073ac78024b6e13f2714db96e9b1e63</email>
              <email type="domain">inria.fr</email>
            </editor>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2019-05-03 13:26:54</date>
              <date type="whenModified">2019-05-03 15:00:20</date>
              <date type="whenReleased">2019-05-03 15:00:20</date>
              <date type="whenProduced">2018-11-02</date>
              <date type="whenEndEmbargoed">2021-01-01</date>
              <ref type="file" target="https://inria.hal.science/hal-02118829v1/document">
                <date notBefore="2021-01-01"/>
              </ref>
              <ref type="file" subtype="author" n="1" target="https://inria.hal.science/hal-02118829v1/file/474230_1_En_5_Chapter.pdf" id="file-2118829-2113779">
                <date notBefore="2021-01-01"/>
              </ref>
              <ref type="externalLink" target="http://arxiv.org/pdf/1805.01288"/>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="200187">
                <persName>
                  <forename>Hal</forename>
                  <surname>Ifip</surname>
                </persName>
                <email type="md5">2073ac78024b6e13f2714db96e9b1e63</email>
                <email type="domain">inria.fr</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-02118829</idno>
            <idno type="halUri">https://inria.hal.science/hal-02118829</idno>
            <idno type="halBibtex">lu:hal-02118829</idno>
            <idno type="halRefHtml">&lt;i&gt;2nd International Conference on Intelligence Science (ICIS)&lt;/i&gt;, Nov 2018, Beijing, China. pp.37-48, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-030-01313-4_5"&gt;&amp;#x27E8;10.1007/978-3-030-01313-4_5&amp;#x27E9;&lt;/a&gt;</idno>
            <idno type="halRef">2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.37-48, &amp;#x27E8;10.1007/978-3-030-01313-4_5&amp;#x27E9;</idno>
            <availability status="restricted">
              <licence target="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 - Attribution<ref corresp="#file-2118829-2113779"/></licence>
            </availability>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="IFIP">IFIP - International Federation for Information Processing</idno>
            <idno type="stamp" n="IFIP-AICT" corresp="IFIP">IFIP Advances in Information and Communication Technology</idno>
            <idno type="stamp" n="IFIP-TC" corresp="IFIP">IFIP Technical Committees </idno>
            <idno type="stamp" n="IFIP-TC12" corresp="IFIP-TC">TC12 - Artificial Intelligence</idno>
            <idno type="stamp" n="IFIP-ICIS" corresp="IFIP">International Conference on Intelligence Science</idno>
            <idno type="stamp" n="IFIP-AICT-539" corresp="IFIP-AICT">Intelligence Science II Third IFIP TC 12 International Conference, ICIS 2018</idno>
          </seriesStmt>
          <notesStmt>
            <note type="commentary">Part 2: Machine Learning</note>
            <note type="audience" n="2">International</note>
            <note type="invited" n="0">No</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
            <note type="proceedings" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">Semantic Channel and Shannon’s Channel Mutually Match for Multi-label Classification</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Chenguang</forename>
                    <surname>Lu</surname>
                  </persName>
                  <email type="md5">6468fcb92f13ddd3293c5bd281dfe3de</email>
                  <email type="domain">foxmail.com</email>
                  <idno type="idhal" notation="numeric">1033431</idno>
                  <idno type="halauthorid" notation="string">1385620-1033431</idno>
                  <affiliation ref="#struct-557661"/>
                </author>
              </analytic>
              <monogr>
                <title level="m">IFIP Advances in Information and Communication Technology</title>
                <meeting>
                  <title>2nd International Conference on Intelligence Science (ICIS)</title>
                  <date type="start">2018-11-02</date>
                  <date type="end">2018-11-05</date>
                  <settlement>Beijing</settlement>
                  <country key="CN">China</country>
                </meeting>
                <editor>Zhongzhi Shi</editor>
                <editor>Cyriel Pennartz</editor>
                <editor>Tiejun Huang</editor>
                <imprint>
                  <publisher>Springer International Publishing</publisher>
                  <biblScope unit="serie">Intelligence Science II</biblScope>
                  <biblScope unit="volume">AICT-539</biblScope>
                  <biblScope unit="pp">37-48</biblScope>
                  <date type="datePub">2018</date>
                </imprint>
              </monogr>
              <idno type="doi">10.1007/978-3-030-01313-4_5</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">Semi-supervised learning</term>
                <term xml:lang="en">Membership function</term>
                <term xml:lang="en">Multi-label classification</term>
                <term xml:lang="en">Semantic information</term>
                <term xml:lang="en">Natural language processing</term>
                <term xml:lang="en">Bayes’ theorem</term>
                <term xml:lang="en">Shannon’s channel</term>
              </keywords>
              <classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
              <classCode scheme="halTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halOldTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halTreeTypology" n="COMM">Conference papers</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>A semantic channel consists of a set of membership functions or truth functions which indicate the denotations of a set of labels. In the multi-label learning, we obtain a semantic channel from a sampling distribution or Shannon’s channel. If samples are huge, we can directly convert a Shannon’s channel into a semantic channel by the third kind of Bayes’ theorem; otherwise, we can optimize the membership functions by a generalized Kullback–Leibler formula. In the multi-label classification, we partition an instance space with the maximum semantic information criterion, which is a special Regularized Least Squares (RLS) criterion and is equivalent to the maximum likelihood criterion. To simplify the learning, we may only obtain the truth functions of some atomic labels to construct the truth functions of compound labels. In a label’s learning, instances are divided into three kinds (positive, negative, and unclear) instead of two kinds as in the One-vs-Rest or Binary Relevance (BR) method. Every label’s learning is independent as in the BR method. However, it is allowed to train a label without negative examples and a number of binary classifications are not used. In the label selection, for an instance, the classifier selects a compound label with the most semantic information. This classifier has taken into the consideration the correlation between labels already. As a predictive model, the semantic channel does not change with the prior probability distribution (source) of instances. It still works when the source is changed. The classifier will vary with the source and hence can overcome the class-imbalance problem. It is shown that the old population’s increase will change the classifier for label “Old person” and has been impelling the evolution of the semantic meaning of “Old”. The CM iteration algorithm for unseen instance classification is introduced.</p>
            </abstract>
            <particDesc>
              <org type="consortium">TC 12</org>
            </particDesc>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="institution" xml:id="struct-557661" status="VALID">
          <orgName>Liaoning Technical University [Fuxin]</orgName>
          <orgName type="acronym">LNTU</orgName>
          <desc>
            <address>
              <country key="CN"/>
            </address>
            <ref type="url">http://en.lntu.edu.cn/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>