{"id":318,"date":"2023-01-15T15:23:24","date_gmt":"2023-01-15T13:23:24","guid":{"rendered":"https:\/\/panagiotis-filippakis.pro\/?p=318"},"modified":"2025-06-10T22:26:29","modified_gmt":"2025-06-10T19:26:29","slug":"publication-in-neurocomputing","status":"publish","type":"post","link":"https:\/\/panagiotis-filippakis.pro\/index.php\/2023\/01\/15\/publication-in-neurocomputing\/","title":{"rendered":"Publication in Neurocomputing"},"content":{"rendered":"<div id=\"pl-318\"  class=\"panel-layout\" ><div id=\"pg-318-0\"  class=\"panel-grid panel-no-style\" ><div id=\"pgc-318-0-0\"  class=\"panel-grid-cell\" ><div id=\"panel-318-0-0-0\" class=\"so-panel widget widget_sow-editor panel-first-child panel-last-child\" data-index=\"0\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t><h3 class=\"widget-title\">Data reduction via generation of multi-label prototypes<\/h3>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<p>A very common practice to speed up instance based classifiers is to reduce the size of their training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen. This can be achieved by applying a Prototype Selection or Generation algorithm, also referred to as a Data Reduction Technique. Most of these techniques cannot be applied on multi-label problems, where an instance may belong to more than one classes. Reduction through Homogeneous Clustering (RHC) and Reduction by Space Partitioning (RSP3) are parameter-free single-label Prototype Generation algorithms. Both are based on recursive data partitioning procedures that identify homogeneous clusters of training data, which they replace by their representatives.<\/p>\n<p>Link in ResearchGate:<a href=\"https:\/\/www.researchgate.net\/profile\/Panagiotis-Filippakis\">Panagiotis Filippakis on ResearchGate<\/a><\/p>\n<p>#artificialintelligence\u00a0#datamining<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>A very common practice to speed up instance based classifiers is to reduce the size of their training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen. This can be achieved by applying a Prototype Selection or Generation algorithm, also referred to as a Data Reduction Technique. Most&hellip;&nbsp;<a href=\"https:\/\/panagiotis-filippakis.pro\/index.php\/2023\/01\/15\/publication-in-neurocomputing\/\" rel=\"bookmark\">\u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 &raquo;<span class=\"screen-reader-text\">Publication in Neurocomputing<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":430,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"off","neve_meta_content_width":70,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-318","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"jetpack_featured_media_url":"https:\/\/panagiotis-filippakis.pro\/wp-content\/uploads\/2023\/01\/neurocomputing-e1746900012239.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts\/318","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/comments?post=318"}],"version-history":[{"count":19,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts\/318\/revisions"}],"predecessor-version":[{"id":545,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts\/318\/revisions\/545"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/media\/430"}],"wp:attachment":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/media?parent=318"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/categories?post=318"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/tags?post=318"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}