{"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/a-novel-online-stacked-ensemble-for-multi","title":"A Novel Online Stacked Ensemble for Multi-Label Stream Classification","arxiv_id":"1809.09994","date":"2018-09-26","proceeding":null,"authors":["Alican Büyükçakır","Hamed Bonab","Fazli Can"],"abstract":"As data streams become more prevalent, the necessity for online algorithms\nthat mine this transient and dynamic data becomes clearer. Multi-label data\nstream classification is a supervised learning problem where each instance in\nthe data stream is classified into one or more pre-defined sets of labels. Many\nmethods have been proposed to tackle this problem, including but not limited to\nensemble-based methods. Some of these ensemble-based methods are specifically\ndesigned to work with certain multi-label base classifiers; some others employ\nonline bagging schemes to build their ensembles. In this study, we introduce a\nnovel online and dynamically-weighted stacked ensemble for multi-label\nclassification, called GOOWE-ML, that utilizes spatial modeling to assign\noptimal weights to its component classifiers. Our model can be used with any\nexisting incremental multi-label classification algorithm as its base\nclassifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles\nand 7 baseline models on 7 real-world datasets from diverse areas of interest.\nOur experiments show that GOOWE-ML ensembles yield consistently better results\nin terms of predictive performance in almost all of the datasets, with respect\nto the other prominent ensemble models.","url_abs":"http://arxiv.org/abs/1809.09994v1","url_pdf":"http://arxiv.org/pdf/1809.09994v1.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":"a-novel-online-stacked-ensemble-for-multi","repo_url":"https://github.com/abuyukcakir/gooweml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}