Towards Utility Maximization in Regression
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 |
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