{"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/scalable-and-accurate-online-feature","title":"Scalable and Accurate Online Feature Selection for Big Data","arxiv_id":"1511.09263","date":"2015-11-30","proceeding":null,"authors":["Kui Yu","Xindong Wu","Wei Ding","Jian Pei"],"abstract":"Feature selection is important in many big data applications. Two critical\nchallenges closely associate with big data. Firstly, in many big data\napplications, the dimensionality is extremely high, in millions, and keeps\ngrowing. Secondly, big data applications call for highly scalable feature\nselection algorithms in an online manner such that each feature can be\nprocessed in a sequential scan. We present SAOLA, a Scalable and Accurate\nOnLine Approach for feature selection in this paper. With a theoretical\nanalysis on bounds of the pairwise correlations between features, SAOLA employs\nnovel pairwise comparison techniques and maintain a parsimonious model over\ntime in an online manner. Furthermore, to deal with upcoming features that\narrive by groups, we extend the SAOLA algorithm, and then propose a new\ngroup-SAOLA algorithm for online group feature selection. The group-SAOLA\nalgorithm can online maintain a set of feature groups that is sparse at the\nlevels of both groups and individual features simultaneously. An empirical\nstudy using a series of benchmark real data sets shows that our two algorithms,\nSAOLA and group-SAOLA, are scalable on data sets of extremely high\ndimensionality, and have superior performance over the state-of-the-art feature\nselection methods.","url_abs":"http://arxiv.org/abs/1511.09263v4","url_pdf":"http://arxiv.org/pdf/1511.09263v4.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":"scalable-and-accurate-online-feature","repo_url":"https://github.com/Vikram-93/Academic-Project-1---Weather-Forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}