{"id":521,"date":"2016-07-17T17:42:17","date_gmt":"2016-07-17T20:42:17","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=521"},"modified":"2016-07-17T17:42:17","modified_gmt":"2016-07-17T20:42:17","slug":"vol9-no1-art6","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/","title":{"rendered":"Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados<\/p>\n<p><strong>Autores:<\/strong> Botelho, Glenda Michele; Batista Neto, Jo\u00e3o<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> A sele\u00e7\u00e3o de caracter\u00edsticas \u00e9 fundamental para minimizar os problemas causados pela alta dimensionalidade. Existem diversos m\u00e9todos tradicionais de sele\u00e7\u00e3o que se baseiam em an\u00e1lises estat\u00edsticas dos dados ou redes neurais. Nestes, a qualidade do subconjunto selecionado \u00e9 dada por meio de alguma fun\u00e7\u00e3o crit\u00e9rio. O presente trabalho prop\u00f5e a inclus\u00e3o de t\u00e9cnicas de Minera\u00e7\u00e3o Visual de Dados, particularmente a proje\u00e7\u00e3o de dados multidimensionais, para apoiar o processo de sele\u00e7\u00e3o. Os resultados mostram que a t\u00e9cnica \u00e9 capaz de prover boa redu\u00e7\u00e3o no espa\u00e7o de caracter\u00edsticas, ao mesmo tempo que mant\u00e9m a capacidade de discrimina\u00e7\u00e3o. A qualidade dos subconjuntos selecionados \u00e9 comprovada tanto quantitativamente pela medida de silhueta quanto pela qualidade visual das proje\u00e7\u00f5es obtidas.<\/p>\n<p><strong>Palavras-chave:<\/strong> Sele\u00e7\u00e3o de caracter\u00edsticas; proje\u00e7\u00e3o de dados multidimensionais; silhueta; agrupamento<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 10<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol9-no1-art6\">10.21528\/lmln-vol9-no1-art6<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol9-no1-art6.pdf\" rel=\"\">vol9-no1-art6.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol9-no1-art6.bib\" rel=\"\">vol9-no1-art6.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados Autores: Botelho, Glenda Michele; Batista Neto, Jo\u00e3o Resumo: A sele\u00e7\u00e3o de caracter\u00edsticas \u00e9 fundamental para minimizar os problemas causados pela alta dimensionalidade. Existem diversos m\u00e9todos tradicionais de sele\u00e7\u00e3o que se <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":509,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-521","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados Autores: Botelho, Glenda Michele; Batista Neto, Jo\u00e3o Resumo: A sele\u00e7\u00e3o de caracter\u00edsticas \u00e9 fundamental para minimizar os problemas causados pela alta dimensionalidade. 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Existem diversos m\u00e9todos tradicionais de sele\u00e7\u00e3o que se Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/","og_site_name":"Learning and NonLinear Models","twitter_card":"summary_large_image","twitter_misc":{"Est. tempo de leitura":"1 minuto"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/","name":"Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"datePublished":"2016-07-17T20:42:17+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/vol9-no1-art6\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Browse issues","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/"},{"@type":"ListItem","position":2,"name":"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 9 &#8211; N\u00famero 1","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol9-no1\/"},{"@type":"ListItem","position":3,"name":"Sele\u00e7\u00e3o de Caracter\u00edsticas Apoiada por Minera\u00e7\u00e3o Visual de Dados"}]},{"@type":"WebSite","@id":"https:\/\/sbia.org.br\/lnlm\/#website","url":"https:\/\/sbia.org.br\/lnlm\/","name":"Learning and NonLinear Models","description":"","publisher":{"@id":"https:\/\/sbia.org.br\/lnlm\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/sbia.org.br\/lnlm\/#organization","name":"Learning and NonLinear Models","url":"https:\/\/sbia.org.br\/lnlm\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","width":398,"height":94,"caption":"Learning and NonLinear Models"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/comments?post=521"}],"version-history":[{"count":0,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/521\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/509"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}