{"id":1860,"date":"2026-10-06T15:31:36","date_gmt":"2026-10-06T15:31:36","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1860"},"modified":"2026-10-06T15:55:28","modified_gmt":"2026-10-06T15:55:28","slug":"vol24-no1-art2","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/","title":{"rendered":"Data Labeling with Local and Global Consistency Using Phylograms"},"content":{"rendered":"<p>Breno Caetano da Silva <a href=\"https:\/\/orcid.org\/0009-0004-1339-6291\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Jorge Luiz Franco <a href=\"https:\/\/orcid.org\/0009-0001-3026-2840\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Hermes Manoel Galv\u00e3o Castelo Branco <a href=\"https:\/\/orcid.org\/0000-0003-4921-7035\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a> &amp; Alexandre Claudio Botazzo Delbem <a href=\"https:\/\/orcid.org\/0000-0003-1810-1742\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> One of the main challenges in machine learning tasks is unlabeled data. Consequently, semi-supervised learning is an approach commonly used in labeling data tasks. The use of diffusion kernels is one of the powerful methods in this set of learning. This paper introduces Phylogram based Data Labeling (PhyDL), an innovative method that combines the benefits of phylogram analysis with Learning with Local and Global Consistency (LLGC). PhyDL leverages phylogram analysis to act as an aggregator of information from labeled samples, capable of correcting possible labeling errors. PhyDL constructs a phylogram using community detection algorithms, FastGreedy and Edge Betweenness, to identify and leverage the structure of the data, with FastGreedy emerging as the best-performing algorithm among those evaluated. We implemented PhyDL and evaluated its performance on the TwoMoons, MNIST, MUSK, HEART, and SONAR datasets. PhyDL outperformed LLGC on 4 out of 5 evaluated datasets, highlighting its effectiveness in semi-supervised learning scenarios. On the TwoMoons, MNIST, MUSK, and SONAR benchmarks, the method showed steady accuracy gains, with consistent improvements as the number of labeled samples increased. However, on the HEART dataset, standard LLGC achieved better results, indicating that PhyDL\u2019s community-based diffusion can be limited by certain underlying spatial structures. Furthermore, the experiments revealed that the Edge Betweenness algorithm is unsuitable for these specific high-dimensional tasks. Ultimately, this study showcases PhyDL\u2019s potential, when equipped with the FastGreedy algorithm, to significantly improve data labeling performance in semi-supervised scenarios, effectively leveraging the structural topology of complex datasets.<\/p>\n<p><strong>Keywords:<\/strong> Local and Global Consistency, DAMICORE, Phylogram, Community Detection, Diffusion Kernel, Semi-Supervised Learning.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol24-no1-art2\">10.21528\/lnlm-vol24-no1-art2<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2026\/12\/vol24-no1-art2.pdf\">vol24-no1-art2.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2026\/12\/vol24-no1-art2.bib\">vol24-no1-art2.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Breno Caetano da Silva , Jorge Luiz Franco , Hermes Manoel Galv\u00e3o Castelo Branco &amp; Alexandre Claudio Botazzo Delbem Abstract: One of the main challenges in machine learning tasks is unlabeled data. Consequently, semi-supervised learning is an approach commonly used <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1851,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1860","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Labeling with Local and Global Consistency Using Phylograms - 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\/vol24-no1\/vol24-no1-art2\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Labeling with Local and Global Consistency Using Phylograms - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Breno Caetano da Silva , Jorge Luiz Franco , Hermes Manoel Galv\u00e3o Castelo Branco &amp; Alexandre Claudio Botazzo Delbem Abstract: One of the main challenges in machine learning tasks is unlabeled data. 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Consequently, semi-supervised learning is an approach commonly used Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/","og_site_name":"Learning and NonLinear Models","article_modified_time":"2026-10-06T15:55:28+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. tempo de leitura":"3 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/","name":"Data Labeling with Local and Global Consistency Using Phylograms - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"primaryImageOfPage":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/#primaryimage"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/#primaryimage"},"thumbnailUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","datePublished":"2026-10-06T15:31:36+00:00","dateModified":"2026-10-06T15:55:28+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/"]}]},{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/#primaryimage","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg"},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art2\/#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 24 &#8211; N\u00famero 1","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/"},{"@type":"ListItem","position":3,"name":"Data Labeling with Local and Global Consistency Using Phylograms"}]},{"@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\/1860","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=1860"}],"version-history":[{"count":1,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1860\/revisions"}],"predecessor-version":[{"id":1861,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1860\/revisions\/1861"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1851"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=1860"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}