{"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-budget-constrained-inverse-classification","title":"A budget-constrained inverse classification framework for smooth classifiers","arxiv_id":"1605.09068","date":"2016-05-29","proceeding":null,"authors":["Michael T. Lash","Qihang Lin","W. Nick Street","Jennifer G. Robinson"],"abstract":"Inverse classification is the process of manipulating an instance such that\nit is more likely to conform to a specific class. Past methods that address\nsuch a problem have shortcomings. Greedy methods make changes that are overly\nradical, often relying on data that is strictly discrete. Other methods rely on\ncertain data points, the presence of which cannot be guaranteed. In this paper\nwe propose a general framework and method that overcomes these and other\nlimitations. The formulation of our method can use any differentiable\nclassification function. We demonstrate the method by using logistic regression\nand Gaussian kernel SVMs. We constrain the inverse classification to occur on\nfeatures that can actually be changed, each of which incurs an individual cost.\nWe further subject such changes to fall within a certain level of cumulative\nchange (budget). Our framework can also accommodate the estimation of\n(indirectly changeable) features whose values change as a consequence of\nactions taken. Furthermore, we propose two methods for specifying feature-value\nranges that result in different algorithmic behavior. We apply our method, and\na proposed sensitivity analysis-based benchmark method, to two freely available\ndatasets: Student Performance from the UCI Machine Learning Repository and a\nreal world cardiovascular disease dataset. The results obtained demonstrate the\nvalidity and benefits of our framework and method.","url_abs":"http://arxiv.org/abs/1605.09068v3","url_pdf":"http://arxiv.org/pdf/1605.09068v3.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-budget-constrained-inverse-classification","repo_url":"https://github.com/michael-lash/BCIC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}