{"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/efsis-ensemble-feature-selection-integrating","title":"EFSIS: Ensemble Feature Selection Integrating Stability","arxiv_id":"1811.07939","date":"2018-11-19","proceeding":null,"authors":["Xiaokang Zhang","Inge Jonassen"],"abstract":"Ensemble learning that can be used to combine the predictions from multiple\nlearners has been widely applied in pattern recognition, and has been reported\nto be more robust and accurate than the individual learners. This ensemble\nlogic has recently also been more applied in feature selection. There are\nbasically two strategies for ensemble feature selection, namely data\nperturbation and function perturbation. Data perturbation performs feature\nselection on data subsets sampled from the original dataset and then selects\nthe features consistently ranked highly across those data subsets. This has\nbeen found to improve both the stability of the selector and the prediction\naccuracy for a classifier. Function perturbation frees the user from having to\ndecide on the most appropriate selector for any given situation and works by\naggregating multiple selectors. This has been found to maintain or improve\nclassification performance. Here we propose a framework, EFSIS, combining these\ntwo strategies. Empirical results indicate that EFSIS gives both high\nprediction accuracy and stability.","url_abs":"http://arxiv.org/abs/1811.07939v1","url_pdf":"http://arxiv.org/pdf/1811.07939v1.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":"efsis-ensemble-feature-selection-integrating","repo_url":"https://github.com/zhxiaokang/EFSIS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}