Towards Utility Maximization in Regression

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

ribeiro, rp (Author) FCUP

Conference proceedings International

Title: 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)

Pages: 179-186

12th IEEE International Conference on Data Mining (ICDM)

Brussels, BELGIUM, DEC 10-13, 2012

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-005-KEW

DOI: 10.1109/icdmw.2012.82

Abstract (EN): Utilitybased 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. We illustrate the objective of our evaluation methodology on a real-life application and also carry a set of experiments over a subset of our target regression tasks: the prediction of rare and extreme values. Results show the effectiveness of our proposed utility metric for identifying the models that perform better on this type of applications.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8