{"id":22773,"date":"2023-02-08T10:39:22","date_gmt":"2023-02-08T15:39:22","guid":{"rendered":"https:\/\/carleton.ca\/math\/?p=22773"},"modified":"2023-02-15T15:35:15","modified_gmt":"2023-02-15T20:35:15","slug":"probability-statistics-seminar-5","status":"publish","type":"post","link":"https:\/\/carleton.ca\/math\/2023\/probability-statistics-seminar-5\/","title":{"rendered":"Probability & Statistics Seminar-Virtual"},"content":{"rendered":"

Title: Grafted and Vanishing Random Subspaces.
\nSpeaker: Tanzy Love, University of Rochester https:\/\/www.urmc.rochester.edu\/biostat\/people\/faculty\/love.aspx<\/a>
\nLocation: Virtual (via zoom\u00a0https:\/\/carleton-ca.zoom.us\/j\/96943828616<\/a>\u00a0)
\nDate: Friday, February 17, 2023
\nTime: 1:00 \u2013 2:00 pm<\/p>\n

Abstract<\/strong>:\u00a0 The Random Subspace Method (RSM) is an\u00a0ensemble procedure in which each constituent learner is\u00a0constructed using a randomly chosen subset of the data\u00a0features. Regression trees are ideal candidate learners\u00a0in RSM ensembles. By constructing trees upon different\u00a0feature subsets, RSM reduces correlation between trees\u00a0resulting in a stronger ensemble. Furthermore, it lessens\u00a0computational burden by only considering a subset of\u00a0the features when building each tree.\u00a0Despite its apparent advantages, RSM has a notable\u00a0drawback. In some instances a randomly chosen subspace\u00a0\u00a0may lack informative features. This is especially\u00a0true in situations in which the number of truly\u00a0informative variables is small relative to the total number\u00a0of variables. Trees that are constructed using feature\u00a0subsets lacking informative features can be damaging\u00a0to the ensemble.<\/p>\n

Here we present Grafted Random Subspaces (GRS)\u00a0and Vanishing Random Subspaces (VRS), two novel\u00a0\u00a0ensemble procedures designed to remedy the\u00a0aforementioned drawback by reusing information across trees.\u00a0Both techniques borrow from RSM by growing individual\u00a0trees on randomly selected feature subsets. For each\u00a0tree in a GRS ensemble, the most important variable\u00a0is identified and guaranteed inclusion into the next q\u00a0feature subsets. This allows GRS to recycle a promising\u00a0feature from one tree across several successive trees, effectively grafting the variable into the next q active\u00a0subsets. In the VRS procedure the least important\u00a0feature is guaranteed exclusion from the next q feature\u00a0subsets. This creates a more enriched pool of candidate\u00a0variables from which the successive feature subsets are\u00a0drawn.<\/p>\n","protected":false},"excerpt":{"rendered":"

Title: Grafted and Vanishing Random Subspaces. Speaker: Tanzy Love, University of Rochester https:\/\/www.urmc.rochester.edu\/biostat\/people\/faculty\/love.aspx Location: Virtual (via zoom\u00a0https:\/\/carleton-ca.zoom.us\/j\/96943828616\u00a0) Date: Friday, February 17, 2023 Time: 1:00 \u2013 2:00 pm Abstract:\u00a0 The Random Subspace Method (RSM) is an\u00a0ensemble procedure in which each constituent learner is\u00a0constructed using a randomly chosen subset of the data\u00a0features. Regression trees are ideal candidate […]<\/p>\n","protected":false},"author":44,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1,3],"tags":[],"yoast_head":"\nProbability & Statistics Seminar-Virtual - School of Mathematics and Statistics<\/title>\n<meta name=\"description\" content=\"Title: Grafted and Vanishing Random Subspaces. 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