{"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/graph-based-selective-outlier-ensembles","title":"Graph-based Selective Outlier Ensembles","arxiv_id":"1804.06378","date":"2018-04-17","proceeding":null,"authors":["Hamed Sarvari","Carlotta Domeniconi","Giovanni Stilo"],"abstract":"An ensemble technique is characterized by the mechanism that generates the\ncomponents and by the mechanism that combines them. A common way to achieve the\nconsensus is to enable each component to equally participate in the aggregation\nprocess. A problem with this approach is that poor components are likely to\nnegatively affect the quality of the consensus result. To address this issue,\nalternatives have been explored in the literature to build selective classifier\nand cluster ensembles, where only a subset of the components contributes to the\ncomputation of the consensus. Of the family of ensemble methods, outlier\nensembles are the least studied. Only recently, the selection problem for\noutlier ensembles has been discussed. In this work we define a new graph-based\nclass of ranking selection methods. A method in this class is characterized by\ntwo main steps: (1) Mapping the rankings onto a graph structure; and (2) Mining\nthe resulting graph to identify a subset of rankings. We define a specific\ninstance of the graph-based ranking selection class. Specifically, we map the\nproblem of selecting ensemble components onto a mining problem in a graph. An\nextensive evaluation was conducted on a variety of heterogeneous data and\nmethods. Our empirical results show that our approach outperforms\nstate-of-the-art selective outlier ensemble techniques.","url_abs":"http://arxiv.org/abs/1804.06378v1","url_pdf":"http://arxiv.org/pdf/1804.06378v1.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":"graph-based-selective-outlier-ensembles","repo_url":"https://github.com/HamedSarvari/Graph-Based-Selective-Outlier-Ensembles","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"outlier-ensembles","task_name":"outlier ensembles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}