Pesquisa de referências

Rough-Fuzzy Support Vector Clustering with OWA Operators

<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a2200000   4500</leader>
    <controlfield tag="001">MAP20220013901</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20220911185521.0</controlfield>
    <controlfield tag="008">220509e20220502esp|||p      |0|||b|spa d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">922.134</subfield>
    </datafield>
    <datafield tag="100" ind1="1" ind2=" ">
      <subfield code="0">MAPA20220004800</subfield>
      <subfield code="a">Saltos Atiencia, Ramiro</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Rough-Fuzzy Support Vector Clustering with OWA Operators</subfield>
      <subfield code="c">Ramiro Saltos Atiencia, Richard Weber</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm, which has been used already in many real-world applications. RFSVC's strengths are its ability to handle arbitrary cluster shapes, identify the number of clusters, and e?ectively detect outliers by the means of membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers' membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing ?nal membership degrees and at the same time allows a better interpretation of the cluster structures found. Particularly, we propose the induced OWA using weights determined by the employed kernel function. The computational experiments show that our approach outperforms the current version of RFSVC as well as alternative techniques ?xing the weights of the OWA operator while maintaining the level of interpretability of membership degrees for detecting outliers.

</subfield>
    </datafield>
    <datafield tag="540" ind1=" " ind2=" ">
      <subfield code="a">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY NC 4.0)"</subfield>
      <subfield code="f"/>
      <subfield code="u">https://creativecommons.org/licenses/by-nc/4.0</subfield>
      <subfield code="9">64</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080611200</subfield>
      <subfield code="a">Inteligencia artificial</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080544638</subfield>
      <subfield code="a">Minería</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20210010477</subfield>
      <subfield code="a">Datos brutos de investigación</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20200017172</subfield>
      <subfield code="a">Análisis vectorial</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20220004817</subfield>
      <subfield code="a">Weber, Richard</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20200034445</subfield>
      <subfield code="g">02/05/2022 Volumen 25 Número 69 - mayo 2022 , p. 42-56</subfield>
      <subfield code="x">1988-3064</subfield>
      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d"> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
    </datafield>
    <datafield tag="856" ind1=" " ind2=" ">
      <subfield code="q">application/pdf</subfield>
      <subfield code="w">1115224</subfield>
      <subfield code="y">Recurso electrónico / Electronic resource</subfield>
    </datafield>
  </record>
</collection>