Towards Utility Maximization in Regression

dc.contributor.author Rita Paula Ribeiro en
dc.date.accessioned 2017-12-21T12:03:52Z
dc.date.available 2017-12-21T12:03:52Z
dc.date.issued 2012 en
dc.description.abstract Utility-based learning is a key technique for addressing many real world data mining applications, where the costs/benefits are not uniform across the domain of the target variable. Still, most of the existing research has been focused on classification problems. In this paper we address a related problem. There are many relevant domains (e.g. ecological, meteorological, finance) where decisions are based on the forecast of a numeric quantity (i.e. the result of a regression model). The goal of the work on this paper is to present an evaluation framework for applications where the numeric outcome of a regression model may lead to different costs/benefits as a consequence of the actions it entails. The new metric provides a more informed estimate of the utility of any regression model, given the application-specific preference biases, and hence makes more reliable the comparison and selection between alternative regression models. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/4620
dc.language eng en
dc.relation 4983 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Towards Utility Maximization in Regression en
dc.type conferenceObject en
dc.type Publication en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
PS-07873.pdf
Size:
633.29 KB
Format:
Adobe Portable Document Format
Description: