{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/can-evolutionary-sampling-improve-bagged","title":"Can Evolutionary Sampling Improve Bagged Ensembles?","arxiv_id":"1610.00465","date":"2016-10-03","proceeding":null,"authors":["Harsh Nisar","Bhanu Pratap Singh Rawat"],"abstract":"Perturb and Combine (P&C) group of methods generate multiple versions of the\npredictor by perturbing the training set or construction and then combining\nthem into a single predictor (Breiman, 1996b). The motive is to improve the\naccuracy in unstable classification and regression methods. One of the most\nwell known method in this group is Bagging. Arcing or Adaptive Resampling and\nCombining methods like AdaBoost are smarter variants of P&C methods. In this\nextended abstract, we lay the groundwork for a new family of methods under the\nP&C umbrella, known as Evolutionary Sampling (ES). We employ Evolutionary\nalgorithms to suggest smarter sampling in both the feature space (sub-spaces)\nas well as training samples. We discuss multiple fitness functions to assess\nensembles and empirically compare our performance against randomized sampling\nof training data and feature sub-spaces.","url_abs":"http://arxiv.org/abs/1610.00465v1","url_pdf":"http://arxiv.org/pdf/1610.00465v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"can-evolutionary-sampling-improve-bagged","repo_url":"https://github.com/evoml/evoml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}