Utility-based regression

• Type: Chapter or Part of a Book
• Date: 2007

Authors

Luis Torgo (Author) FEP; Rita Ribeiro (Author) Other

Book

Title: Knowledge Discovery in Databases: PKDD 2007

Pages: 597-604

ISBN: 978-3-540-74975-2

Electronic ISBN: 978-3-540-74976-9

Publisher: Springer

Indexing

ISI Web of Knowledge - 46 Citations

Scopus - 49 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-DTG

DOI: 10.1007/978-3-540-74976-9_63

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