{"id":260,"date":"2021-10-14T07:45:23","date_gmt":"2021-10-14T04:45:23","guid":{"rendered":"https:\/\/panagiotis-filippakis.pro\/?p=260"},"modified":"2025-06-10T22:26:29","modified_gmt":"2025-06-10T19:26:29","slug":"260","status":"publish","type":"post","link":"https:\/\/panagiotis-filippakis.pro\/index.php\/2021\/10\/14\/260\/","title":{"rendered":"Prototype Generation for Multi-label Nearest Neighbours Classification"},"content":{"rendered":"<div id=\"pl-260\"  class=\"panel-layout\" ><div id=\"pg-260-0\"  class=\"panel-grid panel-no-style\" ><div id=\"pgc-260-0-0\"  class=\"panel-grid-cell\" ><div id=\"panel-260-0-0-0\" 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\">Prototype Generation for Multi-label Nearest Neighbours Classification<\/h3>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<p>Numerous Prototype Selection and Generation algorithms for instance based classi\ufb01ers and single label classi\ufb01cation problems have<br \/>\nbeen proposed in the past and are available in the literature. They build a small set of prototypes that represents as best as possible the initial<br \/>\ntraining data. This set is called the condensing set and has the bene\ufb01t of low computational cost while preserving accuracy. However, the<br \/>\nproposed Prototype Selection and Generation algorithms are not applicable to multi-label problems where an instance may belong to more<br \/>\nthan one classes. The popular Binary Relevance transformation method is also inadequate to be combined with a Prototype Selection or Gener-<br \/>\nation algorithm because of the multiple binary condensing sets it builds. Reduction through Homogeneous Clustering (RHC) is a simple, fast,<br \/>\nparameter-free single label Prototype Generation algorithm that is based on k-means clustering. This paper proposes a RHC variation for multi-<br \/>\nlabel training datasets. The proposed method, called Multi-label RHC (MRHC), inherits all the aforementioned desirable properties of RHC<br \/>\nand generates multi-label prototypes. The experimental study based on nine multi-label datasets shows that MRHC achieves high reduction rates<br \/>\nwithout negatively a\ufb00ecting accuracy.<\/p>\n<p>#artificialintelligence\u00a0#datamining<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Numerous Prototype Selection and Generation algorithms for instance based classi\ufb01ers and single label classi\ufb01cation problems have been proposed in the past and are available in the literature. They build a small set of prototypes that represents as best as possible the initial training data. This set is called the condensing set and has the bene\ufb01t&hellip;&nbsp;<a href=\"https:\/\/panagiotis-filippakis.pro\/index.php\/2021\/10\/14\/260\/\" rel=\"bookmark\">\u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 &raquo;<span class=\"screen-reader-text\">Prototype Generation for Multi-label Nearest Neighbours Classification<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":553,"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-260","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"jetpack_featured_media_url":"https:\/\/panagiotis-filippakis.pro\/wp-content\/uploads\/2021\/10\/HAIS_2021.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts\/260","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=260"}],"version-history":[{"count":22,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts\/260\/revisions"}],"predecessor-version":[{"id":548,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/posts\/260\/revisions\/548"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/media\/553"}],"wp:attachment":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/media?parent=260"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/categories?post=260"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/tags?post=260"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}