{"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/automatic-hyperparameter-tuning-method-for","title":"Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection","arxiv_id":"1902.00567","date":"2019-02-01","proceeding":null,"authors":["Zekun Xu","Deovrat Kakde","Arin Chaudhuri"],"abstract":"In recent years, there have been many practical applications of anomaly\ndetection such as in predictive maintenance, detection of credit fraud, network\nintrusion, and system failure. The goal of anomaly detection is to identify in\nthe test data anomalous behaviors that are either rare or unseen in the\ntraining data. This is a common goal in predictive maintenance, which aims to\nforecast the imminent faults of an appliance given abundant samples of normal\nbehaviors. Local outlier factor (LOF) is one of the state-of-the-art models\nused for anomaly detection, but the predictive performance of LOF depends\ngreatly on the selection of hyperparameters. In this paper, we propose a novel,\nheuristic methodology to tune the hyperparameters in LOF. A tuned LOF model\nthat uses the proposed method shows good predictive performance in both\nsimulations and real data sets.","url_abs":"http://arxiv.org/abs/1902.00567v1","url_pdf":"http://arxiv.org/pdf/1902.00567v1.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":"automatic-hyperparameter-tuning-method-for","repo_url":"https://github.com/vsatyakumar/automatic-local-outlier-factor-tuning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}