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Utility-based regression

Title
Utility-based regression
Type
Chapter or Part of a Book
Year
2007
Authors
Luis Torgo
(Author)
FEP
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Rita Ribeiro
(Author)
Other
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Book
Pages: 597-604
ISBN: 978-3-540-74975-2
Electronic ISBN: 978-3-540-74976-9
Scientific classification
FOS: Natural sciences > Computer and information sciences
Other information
Authenticus ID: P-004-DTG
Abstract (EN): Cost-sensitive learning is a key technique for addressing many real world data mining applications. Most existing research has been focused on classification problems. In this paper we propose a framework for evaluating regression models in applications with non-uniform costs and benefits across the domain of the continuous target variable. Namely, we describe two metrics for asserting the costs and benefits of the predictions of any model given a set of test cases. We illustrate the use of our metrics in the context of a specific type of applications where non-uniform costs are required: the prediction of rare extreme values of a continuous target variable. Our experiments provide clear evidence of the utility of the proposed framework for evaluating the merits of any model in this class of regression domains.
Language: English
Type (Professor's evaluation): Scientific
Notes: Part of the Lecture Notes in Computer Science book series (LNCS, volume 4702) Print ISBN: 978-3-540-74975-2
No. of pages: 8
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