{"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/a-survey-of-predictive-modelling-under","title":"A Survey of Predictive Modelling under Imbalanced Distributions","arxiv_id":"1505.01658","date":"2015-05-07","proceeding":null,"authors":["Paula Branco","Luis Torgo","Rita Ribeiro"],"abstract":"Many real world data mining applications involve obtaining predictive models\nusing data sets with strongly imbalanced distributions of the target variable.\nFrequently, the least common values of this target variable are associated with\nevents that are highly relevant for end users (e.g. fraud detection, unusual\nreturns on stock markets, anticipation of catastrophes, etc.). Moreover, the\nevents may have different costs and benefits, which when associated with the\nrarity of some of them on the available training data creates serious problems\nto predictive modelling techniques. This paper presents a survey of existing\ntechniques for handling these important applications of predictive analytics.\nAlthough most of the existing work addresses classification tasks (nominal\ntarget variables), we also describe methods designed to handle similar problems\nwithin regression tasks (numeric target variables). In this survey we discuss\nthe main challenges raised by imbalanced distributions, describe the main\napproaches to these problems, propose a taxonomy of these methods and refer to\nsome related problems within predictive modelling.","url_abs":"http://arxiv.org/abs/1505.01658v2","url_pdf":"http://arxiv.org/pdf/1505.01658v2.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":"a-survey-of-predictive-modelling-under","repo_url":"https://github.com/smrjan/predictive-maintainance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1505.01658","atlas_url":"https://app.syntology.ai/?focus=1505.01658","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}