{"type":"ScholarlyArticle","title":"Instance-Level Metalearning for Outlier Detection","authors":[{"name":"Long Vu","slug":"long-vu","given":"Long","family":"Vu","affiliation":"IBM Research"},{"name":"Peter Kirchner","slug":"peter-kirchner","given":"Peter","family":"Kirchner","affiliation":"IBM Research"},{"name":"Charu C. Aggarwal","slug":"charu-c-aggarwal","given":"Charu C","family":"Aggarwal","affiliation":"IBM Research"},{"name":"Horst Samulowitz","slug":"horst-samulowitz","given":"Horst","family":"Samulowitz","affiliation":"IBM Research"}],"abstract":"A machine learning task can be viewed as a sequential pipeline of different algorithmic choices, including\r\ndata preprocessing, model selection, and\r\nhyper-parameter tuning. Automated machine learning selects this sequence in an\r\nautomated manner.  While such approaches are natural in supervised settings,\r\n    they remain challenging for unsupervised tasks such as outlier detection because of the lack of availability of label-centric feedback. In this paper, we present an instance-level metalearning approach for outlier detection.  This approach learns how outlier instances are related to normal points in many labeled data sets to create a supervised meta-model. This\r\nmeta-model is then used on a new (unlabeled) data set to predict outliers. We show the robustness of our approach on several benchmarks from the OpenML repository.","doi":"10.24963/ijcai.2024/263","url":"https://ijcai.org/proceedings/2024/263","pdf":"https://ijcai.org/proceedings/2024/0263.pdf","markdown":"https://ijcai.org/proceedings/2024/263.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/263","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":263,"pages":{"first":2379,"last":2387},"keywords":["Machine Learning: ML: Meta-learning","Data Mining: DM: Anomaly/outlier detection","Machine Learning: ML: Automated machine learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Sathe, 2015] Charu Aggarwal and Saket Sathe. 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