{"version":"1.0","provider_name":"In Abstract","provider_url":"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract","author_name":"Enna Bartlett","author_url":"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract\/author\/ennabartlett\/","title":"On the Stability of Feature Selection Algorithms - In Abstract","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"m97AqjEiUn\"><a href=\"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract\/on-the-stability-of-feature-selection-algorithms\/\">On the Stability of Feature Selection Algorithms<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract\/on-the-stability-of-feature-selection-algorithms\/embed\/#?secret=m97AqjEiUn\" width=\"600\" height=\"338\" title=\"&#8220;On the Stability of Feature Selection Algorithms&#8221; &#8212; In Abstract\" data-secret=\"m97AqjEiUn\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/* ]]> *\/\n<\/script>\n","thumbnail_url":"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract\/wp-content\/uploads\/sites\/61\/2018\/08\/On-the-Stability-of-Feature-Selection-Algorithms.jpg","thumbnail_width":890,"thumbnail_height":350,"description":"Quantifying the reproducibility of feature selection research When solving a problem in data science, some pieces of the data are important, some irrelevant, and some only show their worth in the context of others. The task of automatically distinguishing the important features from a large body of data is called &#8221;feature selection&#8221;, and there exist [&hellip;]"}