Rule-based prediction of rare extreme values

• Type: Article in International Scientific Journal
• Date: 2006

Authors

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

Journal

Title: Lecture Notes in Computer Science

Vol. 387

Pages: 219-230

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 1 Citation

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-PH8

DOI: 10.1007/11893318_23

Abstract (EN): This paper describes a rule learning method that obtains models biased towards a particular class of regression tasks. These tasks have as main distinguishing feature the fact that the main goal is to be accurate at predicting rare extreme values of the continuous target variable. Many real-world applications from scientific areas like ecology, meteorology, finance,etc., share this objective. Most existing approaches to regression problems search for the model parameters that optimize a given average error estimator (e.g. mean squared error). This means that they are biased towards achieving a good performance on the most common cases. The motivation for our work is the claim that being accurate at a small set of rare cases requires different error metrics. Moreover, given the nature and relevance of this type of applications an interpretable model is usually of key importance to domain experts, as predicting these rare events is normally associated with costly decisions. Our proposed system (R-PREV) obtains a set of interpretable regression rules derived from a set of bagged regression trees using evaluation metrics that bias the resulting models to predict accurately rare extreme values. We provide an experimental evaluation of our method confirming the advantages of our proposal in terms of accuracy in predicting rare extreme values.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12