Regional Ensemble for Improving Unsupervised Outlier Detectors

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Regional Ensemble for Improving Unsupervised Outlier Detectors 11 Pages Posted: 17 Aug 2022 See all articles by Jiawei YangJiawei YangNorthwestern Polytechnical UniversityDr Sylwan RahardjaUniversity of Eastern FinlandProf Susanto RahardjaNorthwestern Polytechnical University Abstract Unsupervised outlier ensemble is a challenging problem due to the lack of ground truth. We present regionalensemble (RE), a novel algorithm which considers detector-factor, object-factor, value-factor and distribution-factor, while traditional outlier ensembles considered only detector-factor and value-factor, making them a subset of RE. Instead of replacing traditional ensembles, RE can be combined with these ensembles by including object-factor and distribution-factor. RE reduces the outlier detectors' errors in minority subsets by utilizing the outlier scores of objects obtained from the majority. RE provides many potential applications particularly in data mining and machine learning. Compared to existing outlier ensembles with 30real world data sets tested, RE attained best performance with 14 datasets, while the current standard achieves superior performance with only 8 datasets. RE can significantly improve the best existing from 0.83 to 0.86 AUC on average. Keywords: Regional ensemble, outlier detection, Unsupervised outlier ensemble, Nearest neighbor Suggested Citation: Suggested Citation Yang, Jiawei and Rahardja, Dr Sylwan and Rahardja, Prof Susanto, Regional Ensemble for Improving Unsupervised Outlier Detectors. Available at SSRN: https://ssrn.com/abstract=4192366 Jiawei Yang Northwestern Polytechnical University ( email ) Xian Dr Sylwan Rahardja University of Eastern Finland ( email ) PO Box 111Joensuu, 80100Finland Prof Susanto Rahardja (Contact Author) Northwestern Polytechnical University ( email ) Xian Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 0 Abstract Views 1 PlumX Metrics Related eJournals Computation Theory eJournal Follow Computation Theory eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 178 PAPERS 2,987 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.203 seconds