{"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/lscp-locally-selective-combination-in","title":"LSCP: Locally Selective Combination in Parallel Outlier Ensembles","arxiv_id":"1812.01528","date":"2018-12-04","proceeding":null,"authors":["Yue Zhao","Zain Nasrullah","Maciej K. Hryniewicki","Zheng Li"],"abstract":"In unsupervised outlier ensembles, the absence of ground truth makes the\ncombination of base outlier detectors a challenging task. Specifically,\nexisting parallel outlier ensembles lack a reliable way of selecting competent\nbase detectors, affecting accuracy and stability, during model combination. In\nthis paper, we propose a framework---called Locally Selective Combination in\nParallel Outlier Ensembles (LSCP)---which addresses the issue by defining a\nlocal region around a test instance using the consensus of its nearest\nneighbors in randomly selected feature subspaces. The top-performing base\ndetectors in this local region are selected and combined as the model's final\noutput. Four variants of the LSCP framework are compared with seven widely used\nparallel frameworks. Experimental results demonstrate that one of these\nvariants, LSCP_AOM, consistently outperforms baselines on the majority of\ntwenty real-world datasets.","url_abs":"http://arxiv.org/abs/1812.01528v2","url_pdf":"http://arxiv.org/pdf/1812.01528v2.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":"lscp-locally-selective-combination-in","repo_url":"https://github.com/yzhao062/LSCP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"},{"task_slug":"outlier-ensembles","task_name":"outlier ensembles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.01528","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}